AlpacaEval : An Automatic Evaluator for Instruction-following Language Models
AlpacaEval 2.0 with length-controlled win-rates (paper) has a spearman correlation of 0.98 with ChatBot Arena while costing less than $10 of OpenAI credits run and running in less than 3 minutes. Our goal is to have a benchmark for chat LLMs that is: fast (< 5min), cheap (< $10), and highly correlated with humans (0.98). Here's a comparison with other benchmarks:
Updates:
:tada: Length-controlled Win Rates are out and used by default! This increases the correlation with ChatBot Arena from 0.93 to 0.98, while significantly decreasing length gameability. The raw win rates are still shown on the website and the CLI. More details here.
:tada: AlpacaEval 2.0 is out and used by default! We improved the auto-annotator (better and cheaper) and use GPT-4 preview as baseline. More details here. For the old version, set your environment variable IS_ALPACA_EVAL_2=False.
Evaluation of instruction-following models (e.g., ChatGPT) typically requires human interactions. This is time-consuming, expensive, and hard to replicate. AlpacaEval in an LLM-based automatic evaluation that is fast, cheap, replicable, and validated against 20K human annotations. It is particularly useful for model development. Although we improved over prior automatic evaluation pipelines, there are still fundamental limitations like the preference for longer outputs. AlpacaEval provides the following:
When to use AlpacaEval? Our automatic evaluator is a quick and cheap proxy for human evaluation of simple instruction-following tasks. It is useful if you have to run many evaluations quickly, e.g., during model development.
When not to use AlpacaEval? As any other automatic evaluator, AlpacaEval should not replace human evaluation in high-stake decision-making, e.g., to decide on model release. In particular, AlpacaEval is limited by the fact that (1) the instructions in the eval set might not be representative of advanced usage of LLMs; (2) automatic evaluators may have biases such as favoring style over factuality of the answer; and (3) AlpacaEval does not measure the risks that a model could cause. Details in limitations.
To install the stable release, run
pip install alpaca-eval
To install the nightly version, run
pip install git+https://github.com/tatsu-lab/alpaca_eval
Then you can use it as follows:
export OPENAI_API_KEY=<your_api_key> # for more complex configs, e.g. using Azure or switching clients see client_configs/README.md
alpaca_eval --model_outputs 'example/outputs.json'
This will print the leaderboard to the console, and save both the leaderboard and the annotations to the same directory as the model_outputs file. Important parameters are the following:
instruction and output.weighted_alpaca_eval_gpt4_turbo (
default for AlpacaEval 2.0), which has a
high agreement rate with our human annotation data, large context size, and is pretty cheap. For a comparison of all annotators see here.model_outputs. By default, this
is gpt4_turbo for AlpacaEval 2.0.If you don't have the model outputs, you can
use evaluate_from_model and
pass a local path or a name of a
HuggingFace
model, or a model from a standard API (OpenAI, Anthropic, Cohere, google, ...). Other commands:
>>> alpaca_eval -- --helpSYNOPSIS
alpaca_eval COMMAND
COMMANDS
COMMAND is one of the following:
evaluate
Evaluate a model based on its outputs. This is the default entrypoint if no command is specified.
evaluate_from_model
Evaluate a model from HuggingFace or an API provider. This is a wrapper around `evaluate` which includes generating from a desired model.
make_leaderboard
Precompute and save an entire leaderboard for a given dataset / evaluator / set of models generations.
analyze_evaluators
Analyze an evaluator and populates the evaluators leaderboard (agreement with human, speed, price,...).
For more information about each function use alpaca_eval <command> -- --help.
Our leaderboards are computed on the AlpacaEval dataset.
We precomputed the leaderboard for important models using different baseline models and autoannotators.
Our two main leaderboards ("AlpacaEval 2.0" and "AlpacaEval") can be found
on this page.
"AlpacaEval 2.0" uses weighted_alpaca_eval_gpt4_turbo for the annotator and gpt4_turbo for the baseline.
"AlpacaEval" uses alpaca_eval_gpt4 for the annotator and text_davinci_003 for the baseline.
For all precomputed leaderboards see here.
Later we also show how to add your model to the
leaderboard and how to make
a new leaderboard for your evaluator/dataset.
See here for the configs of all
models that are available out of the box.
AlpacaEval minimal leaderboard:
| Win Rate | Std Error | |
|---|---|---|
| gpt4 | 95.3 | 0.7 |
| claude | 88.4 | 1.1 |
| chatgpt | 86.1 | 1.2 |
| guanaco-65b | 71.8 | 1.6 |
| vicuna-13b | 70.4 | 1.6 |
| text_davinci_003 | 50.0 | 0.0 |
| alpaca-farm-ppo-human | 41.2 | 1.7 |
| alpaca-7b | 26.5 | 1.5 |
| text_davinci_001 | 15.2 | 1.2 |
Win Rate: the win rate measures the fraction of time the model's output is preferred over the reference's outputs (test-davinci-003 for AlpacaEval and gpt4_turbo for AlpacaEval 2.0).
More specifically, to compute the win rate we collect pairs of outputs of the desired model on every instruction from
the ApacaEval dataset.
We then pair each output with the output of our reference model (e.g. text-davinci-003) on the same instruction.
We then ask our automatic evaluator which output they prefer.
See AlpacaEval's
and AlpacaEval 2.0's prompts and configs, in particular we randomize the order of
outputs to avoid position bias.
We then average the preferences over all instructions in the dataset to get the win rate of the model over the baseline.
If both outputs are exactly the same we use a half preference for both models.
Standard error: this is the standard error (normalized by N-1) of the win rate, i.e., the preferences averaged over the different instructions.
alpaca_eval_gpt4Our alpaca_eval_gpt4 (
see configs)
annotator averages over preferences, where preferences are obtained as follows:
temperature=0The annotator is a mix between (and was highly influenced by) AlpacaFarm and Aviary evaluators. In particular, we use the same code as for AlpacaFarm (caching/randomization/hyperparameters) but use a ranking prompt similar to that of Aviary. We make changes to Aviary's prompt to decrease the bias for longer outputs. Details in Related work.
For AlpacaEval 2.0 we use weighted_alpaca_eval_gpt4_turbo, which uses logprobs to compute continuous preference and uses GPT4_turbo as model (
see configs).
We evaluate different automatic annotators on the AlpacaEval set by comparing to
2.5K human annotations
we collected (~650 instructions each with 4 human annotations).
Below we show metrics for our suggested evaluators (weighted_alpaca_eval_gpt4_turbo,alpaca_eval_gpt4), for prior
automatic
evaluators (alpaca_farm_greedy_gpt4,aviary_gpt4,lmsys_gpt4),
for humans (humans), and for different base models with essentially the same
prompt (gpt4,claude,text_davinci_003,chatgpt_fn,guanaco_33b, chatgpt).
See here for the configs of all
evaluators that are available out of the box and their associated metrics.
| Human agreement | Price [$/1000 examples] | Time [seconds/1000 examples] | Spearman corr. | Pearson corr. | Bias | Variance | Proba. prefer longer | |
|---|---|---|---|---|---|---|---|---|
| alpaca_eval_gpt4 | 69.2 | 13.6 | 1455 | 0.97 | 0.93 | 28.4 | 14.6 | 0.68 |
| alpaca_eval_cot_gpt4_turbo_fn | 68.6 | 6.3 | 1989 | 0.97 | 0.90 | 29.3 | 18.4 | 0.67 |
| alpaca_eval_gpt4_turbo_fn | 68.1 | 5.5 | 864 | 0.93 | 0.82 | 30.2 | 15.6 | 0.65 |
| alpaca_eval_llama3_70b_fn | 67.5 | 0.4 | 209 | 0.90 | 0.86 | 32.3 | 8.2 | 0.79 |
| gpt4 | 66.9 | 12.5 | 1037 | 0.88 | 0.87 | 31.5 | 14.6 | 0.65 |
| alpaca_farm_greedy_gpt4 | 66.4 | 15.3 | 878 | 0.85 | 0.75 | 30.2 | 19.3 | 0.60 |
| alpaca_eval_cot_gpt4_turbo_fn | 65.7 | 4.3 | 228 | 0.78 | 0.77 | 33.9 | 23.7 | 0.61 |
| humans | 65.7 | 300.0 | 36800 | 1.00 | 1.00 | 0.0 | 34.3 | 0.64 |
| claude | 65.3 | 3.3 | 173 | 0.93 | 0.90 | 32.4 | 18.5 | 0.66 |
| lmsys_gpt4 | 65.3 | 13.9 | 17982 | 0.98 | 0.97 | 31.6 | 15.9 | 0.74 |
| text_davinci_003 | 64.1 | 8.7 | 121 | 0.85 | 0.83 | 33.8 | 22.7 | 0.70 |
| longest | 62.2 | 0.0 | 0 | 0.27 | 0.56 | 37.8 | 0.0 | 1.00 |
| chatgpt | 57.3 | 0.8 | 285 | 0.72 | 0.71 | 39.4 | 34.1 | 0.59 |
We now explain in words how we compute the metrics in the table above. The code is here.
Human agreement: this measures the agreement between the current annotator and the majority preferences of
humans on
our
~650 annotations from
our cross-annotation set,
which contains 4 human annotations per example.
To estimate the agreement between a single human (humans row in the table above) and the majority of humans, we take
one of the 4 annotations and compute the accuracy that it has when predicting the mode of the other 3 annotations.
We then average this accuracy over all 4 annotations and over the 650 instructions to get the human agreement, i.e., we
compute the expected (over humans and samples)
leave-one-out agreement.
If the mode is not unique, we take one of the modes at random.
We perform exactly the same computation for the automatic annotators, so that the final numbers are comparable.
Price [$/1000 examples]: this is the average price of every 1000 annotations. For humans, it is the price that we paid Mechanical Turkers to collect those annotations ($21/hour). If the price depends on the machine used to compute the annotations (e.g. Guanaco) we leave it empty.
Time [seconds/1000 examples]: this is the average time it takes to compute 1000 annotations. For humans, it is the estimated median time that each Mechanical Turker took to annotate 1000 examples. For automatic annotators, it is the average time that it took us when running the annotations. Note that this can depend on API limits that are different for different users and the number of requests that the clusters are processing.
Spearman corr.: this measures the Spearman correlation between a leaderboard computed with the auto-annotator's preference and the leaderboard computed with human preferences. As with Human agreement, we use the human annotations from AlpacaFarm but we now consider the method-level agreement rather than only the sample-wise agreement with humans. Note that we only use have 9 models and so the correlation is not very reliable.
Pearson corr.: same as with Spearman corr. but with Pearson correlation.
Bias: agreement between the most likely human label and the most likely automatic one. For automatic annotators we estimate it by sampling 4 different annotations for each example. The randomness here comes from the order of the outputs in the prompt, sampling from the LLM, and if applicable the order of the instruction in the batch and the choice of annotator in the pool. We then take the mode of the 4 annotations and compute the accuracy of the mode when predicting the mode of the 4 human annotations. Note that this is likely an overestimate on the real bias that we would get if we had an "infinite" number of cross-annotations. A low bias means that the annotator has in expectation the same preferences as humans. For the case of humans, the bias is zero by definition. Note that this is related to but not the standard statistical bias, because we take the mode instead of average over annotations and we consider 0-1 loss instead of squared loss.
Variance: expected agreement a single automatic preference and the most likely one. We estimate it the same way as we estimated "human agreement" for humans, i.e., we take the expected leave one out error when predicting the mode of the 3 annotations using the 4th annotation. A low variance means that the annotator is consistent with its preference, i.e., if you sample from it with different seeds it will give the same result. As with the bias, this is not exactly the standard statistical variance, because we take the mode instead of average over annotations and we consider 0-1 loss instead of squared loss.
Note that the "human agreement" is tightly related to the bias and variance. In particular, the variance measures the error due to the fact that we only use a single annotation while the bias aims to measure the irreducible error for the current annotator.
Proba. prefer longer: this is the probability that the annotator prefers the longer output when one of the two outputs is significantly longer than the other (more than 30 characters difference).
In the full table we also provide the following metrics:
Proba. prefer lists: this is the probability that the annotator prefers the output that contains a list/bullet points when one output does but not the other.
Proba. prefer 1: this is the probability that the annotator prefers the first of the pair of outputs. All our
proposed annotators randomize over outputs in the prompt, so this should be 0.5. Prior annotators, such as lmsys
and aviary, do not.
# parsed: this is the number of examples that the annotator was able to parse.
Note that if the variance and bias is empty, it means that we only performed one single annotation for each 648 example due to resource (time and price) constraints. This explains why the #parsed is 648, otherwise it should be 2592.
Overall we recommend using annotators_config=weighted_alpaca_eval_gpt4_turbo if you want the high agreement with humans, and
annotators_config=chatgpt_fn if you are on a tight budget.
When choosing an annotator we recommend you to consider the following (the first three are obvious):
"Human agreement [%]""Price [$/1000 examples]""Time [seconds/1000 examples]""* corr." approx. > 0.7. It is important that the correlation is not too low, but we do not recommend using it as the main metric as the correlation is computed on only 9 models."Proba. prefer longer" approx. < 0.7. Indeed, we found see that the majority of preference of human annotators have
strong bias for longer answers (as shown by the
high performance=62.2
of
the "longest" evaluator that always
prefers the longest output). This suggests that it might more of a bias with the human annotators. In order to avoid
having leaderboards with strong biases for length, we suggest using automatic annotators with less than 0.7 "Proba.
prefer longer"."Variance" approx. < 0.2. We believe that a good evaluator should have as little variance as possible so that
results are mostly reproducible. Note that variance can be desirable in the case where we are simulating humans
as shown in AlpacaFarm.We filtered the annotators that do not satisfy those requirements in the table above (besides humans / ChatGPT / 003 /
lmsys for
reference purposes). For
all
results see here.
In general, we found weighted_alpaca_eval_gpt4_turbo to be a good trade-off between quality / price / time /
variance / length bias.
The above metrics are computed with respect to annotations from crowd-workers. Although useful, those annotations are not perfect, e.g., crowd-workers often favor style over factuality. We thus recommend users to validate automatic evaluators on their own instructions and human annotations. Details in limitations.
>>> alpaca_eval evaluate -- --helpNAME
alpaca_eval evaluate - Evaluate a model based on its outputs. This is the default entrypoint if no command is specified.
SYNOPSIS
alpaca_eval evaluate <flags>
DESCRIPTION
Evaluate a model based on its outputs. This is the default entrypoint if no command is specified.
FLAGS
--model_outputs=MODEL_OUTPUTS
Type: Optional[Union]
Default: None
The outputs of the model to add to the leaderboard. Accepts data (list of dictionary, pd.dataframe, datasets.Dataset) or a path to read those (json, csv, tsv) or a function to generate those. Each dictionary (or row of dataframe) should contain the keys that are formatted in the prompts. E.g. by default `instruction` and `output` with optional `input`. If None, we just print the leaderboard.
-r, --reference_outputs=REFERENCE_OUTPUTS
Type: Union
Default: <func...
The outputs of the reference model. Same format as `model_outputs`. If None, the reference outputs are a specific set of Davinci 003 outputs on the AlpacaEval set:
--annotators_config=ANNOTATORS_CONFIG
Type: Union
Default: 'alpaca_eval_gpt4_turbo_fn'
The path the (or list of dict of) the annotator's config file. For details see the docstring of `PairwiseAnnotator`.
-n, --name=NAME
Type: Optional[Optional]
Default: None
The name of the model to add to the leaderboard. If None we check if `generator is in model_outputs` if not we use "Current model".
-o, --output_path=OUTPUT_PATH
Type: Union
Default: 'auto'
Path to the directory where the new leaderboard and the annotations should be stored. If None we don't save. If `auto` we use `model_outputs` if it is a path, and otherwise use the directory from which we call the script.
-p, --precomputed_leaderboard=PRECOMPUTED_LEADERBOARD
Type: Union
Default: 'auto'
The precomputed leaderboard or a path to it (json, csv, or tsv). The leaderboard should contain at least the column `win_rate`. If `auto` we will try to use the corresponding leaderboard for the reference outputs (only if in CORRESPONDING_OUTPUTS_LEADERBOARDS). If `None` we won't add other models from the leaderboard.
--is_overwrite_leaderboard=IS_OVERWRITE_LEADERBOARD
Type: bool
Default: False
Whether to overwrite the leaderboard if the model is already in it.
-l, --leaderboard_mode_to_print=LEADERBOARD_MODE_TO_PRINT
Type: Optional
Default: 'minimal'
The mode of the leaderboard to use. Only used if the precomputed leaderboard has a column `mode`, in which case it will filter the leaderboard by this mode. If None keeps all.
-c, --current_leaderboard_mode=CURRENT_LEADERBOARD_MODE
Type: str
Default: 'community'
The mode of the leaderboard for the current method.
--is_return_instead_of_print=IS_RETURN_INSTEAD_OF_PRINT
Type: bool
Default: False
Whether to return the metrics instead of printing the results.
-f, --fn_metric=FN_METRIC
Type: Union
Default: 'pairwise_to_winrate'
The function or function name in `metrics.py` that will be used to convert preference to metrics. The function should take a sequence of preferences (0 for draw, 1 for base win, 2 when the model to compare wins) and return a dictionary of metrics and the key by which to sort the leaderboard.
-s, --sort_by=SORT_BY
Type: str
Default: 'win_rate'
The key by which to sort the leaderboard.
--is_cache_leaderboard=IS_CACHE_LEADERBOARD
Type: Optional[Optional]
Default: None
Whether to save the result leaderboard to `precomputed_leaderboard`. If None we save only if max_instances not None. A preferred way of adding models to the leaderboard is to set `precomputed_leaderboard` to the previously saved leaderboard at `<output_path>/leaderboard.csv`.
--max_instances=MAX_INSTANCES
Type: Optional[Optional]
Default: None
The maximum number of instances to annotate. Useful for testing.
--annotation_kwargs=ANNOTATION_KWARGS
Type: Optional[Optional]
Default: None
Additional arguments to pass to `PairwiseAnnotator.annotate_head2head`.
-A, --Annotator=ANNOTATOR
Default: <class 'alpaca_eval.annotators.pairwise_evaluator.PairwiseAn...
The annotator class to use.
Additional flags are accepted.
Additional arguments to pass to `PairwiseAnnotator`.
>>> alpaca_eval evaluate_from_model -- --helpNAME
alpaca_eval evaluate_from_model - Evaluate a model from HuggingFace or an API provider. This is a wrapper around `evaluate` which includes generating from a desired model.
SYNOPSIS
alpaca_eval evaluate_from_model MODEL_CONFIGS <flags>
DESCRIPTION
Evaluate a model from HuggingFace or an API provider. This is a wrapper around `evaluate` which includes generating from a desired model.
POSITIONAL ARGUMENTS
MODEL_CONFIGS
Type: Union
A dictionary or path (relative to `models_configs`) to a yaml file containing the configuration of the model to decode from. If a directory,we search for 'configs.yaml' in it. The keys in the first dictionary should be the generator's name, and the value should be a dictionary of the generator's configuration which should have the
FLAGS
-r, --reference_model_configs=REFERENCE_MODEL_CONFIGS
Type: Optional[Union]
Default: None
Same as in `model_configs` but for the reference model. If None, we use the default Davinci003 outputs.
-e, --evaluation_dataset=EVALUATION_DATASET
Type: Union
Default: <func...
Path to the evaluation dataset or a function that returns a dataframe. If None, we use the default evaluation
-a, --annotators_config=ANNOTATORS_CONFIG
Type: Union
Default: 'alpaca_eval_gpt4_turbo_fn'
Path to the annotators configuration or a dictionary. If None, we use the default annotators configuration.
-o, --output_path=OUTPUT_PATH
Type: Union
Default: 'auto'
Path to save the generations, annotations and leaderboard. If auto saves at `results/<model_name>`
-m, --max_instances=MAX_INSTANCES
Type: Optional[int]
Default: None
Maximum number of instances to generate and evaluate. If None, we evaluate all instances.
--is_strip_output=IS_STRIP_OUTPUT
Type: bool
Default: True
Whether to strip trailing and leading whitespaces from the outputs.
--is_load_outputs=IS_LOAD_OUTPUTS
Type: bool
Default: True
Whether to try to load outputs from the output path. If True and outputs exist we only generate outputs for instructions that don't have outputs yet.
-c, --chunksize=CHUNKSIZE
Type: int
Default: 64
Number of instances to generate before saving. If None, we save after all generations.
Additional flags are accepted.
Other kwargs to `evaluate`
NOTES
You can also use flags syntax for POSITIONAL ARGUMENTS
To evaluate a model you need to:
model_outputs. By default, we use
the 805 examples from AlpacaEval. To compute outputs on AlpacaEval use:import datasets
eval_set = datasets.load_dataset("tatsu-lab/alpaca_eval", "alpaca_eval")["eval"]
for example in eval_set:
# generate here is a placeholder for your models generations
example["output"] = generate(example["instruction"])
example["generator"] = "my_model" # name of your model
if your model is a HuggingFace model or from a standard API provider (OpenAI, Anthropic, Cohere). Then you can
directly use alpaca_eval evaluate_from_model to also take care of generating outputs.
reference_outputs. By default, we use precomputed outputs of gpt4_turbo on
AlpacaEval.
If you
want to use a different model or a different dataset follow the same steps as (1.).annotators_config. We recommend using alpaca_eval_gpt4_turbo_fn. For other options and comparisons
see this table. Depending on the evaluator you might need to
set the appropriate API_KEY in your environment
or int the client_configs.Running all together:
alpaca_eval --model_outputs 'example/outputs.json' \
--annotators_config 'alpaca_eval_gpt4_turbo_fn'
If you don't have decoded outputs, you can use evaluate_from_model which takes care of decoding (model and reference)
for you.
Here's an
example:
# need a GPU for local models
alpaca_eval evaluate_from_model \
--model_configs 'oasst_pythia_12b' \
--annotators_config 'alpaca_eval_gpt4_turbo_fn'
Here the model_configs and reference_model_configs (optional) are paths to a directory that specifies the prompt,
the model
provider (here HuggingFace) and decoding parameters.
See this directory for examples.
For all model providers that are available out-of-the-box
see here.
caching_path. Annotations are thus never recomputed, which makes annotations faster, cheaper and allow for
reproducibility. This helps even when evaluating different models as many models
have
the same outputs.>>> alpaca_eval make_leaderboard -- --helpNAME
alpaca_eval make_leaderboard - Precompute and save an entire leaderboard for a given dataset / evaluator / set of models generations.
SYNOPSIS
alpaca_eval make_leaderboard <flags>
DESCRIPTION
Precompute and save an entire leaderboard for a given dataset / evaluator / set of models generations.
FLAGS
--leaderboard_path=LEADERBOARD_PATH
Type: Optional[Union]
Default: None
The path to save the leaderboard to. The leaderboard will be saved as a csv file, if it already exists it will
--annotators_config=ANNOTATORS_CONFIG
Type: Union
Default: 'alpaca_eval_gpt4_turbo_fn'
The path the (or list of dict of) the annotator's config file.
--all_model_outputs=ALL_MODEL_OUTPUTS
Type: Union
Default: <fu...
The outputs of all models to add to the leaderboard. Accepts data (list of dictionary, pd.dataframe, datasets.Dataset) or a path to read those (json, csv, tsv potentially with globbing) or a function to generate those. If the path contains a globbing pattern, we will read all files matching the pattern and concatenate them. Each dictionary (or row of dataframe) should contain the keys that are formatted in the prompts. E.g. by default `instruction` and `output` with optional `input`. It should also contain a column `generator` with the name of the current model.
-r, --reference_outputs=REFERENCE_OUTPUTS
Type: Union
Default: <func...
The outputs of the reference model. Same format as `all_model_outputs` but without needing `generator`. By default, the reference outputs are the 003 outputs on AlpacaEval set.
-f, --fn_add_to_leaderboard=FN_ADD_TO_LEADERBOARD
Type: Callable
Default: 'evaluate'
The function to use to add a model to the leaderboard. If a string, it should be the name of a function in `main.py`. The function should take the arguments: `model_outputs`, `annotators_config`, `name`, `precomputed_leaderboard`, `is_return_instead_of_print`, `reference_outputs`.
--leaderboard_mode=LEADERBOARD_MODE
Type: str
Default: 'verified'
The mode of the leaderboard to save all new entries with.
-i, --is_return_instead_of_print=IS_RETURN_INSTEAD_OF_PRINT
Type: bool
Default: False
Whether to return the metrics instead of printing the results.
Additional flags are accepted.
Additional arguments to pass to `fn_add_to_leaderboard`.
If you want to make a new leaderboard using a single command (rather than multiple alpaca_eval calls), for your
desired evaluation
set and evaluators, you can use the following:
alpaca_eval make_leaderboard \
--leaderboard_path <path_to_save_leaderboard> \
--all_model_outputs <model_outputs_path> \
--reference_outputs <reference_outputs_path> \
--annotators_config <path_to_config.yaml>
where:
leaderboard_path: path to save the leaderboard to. The leaderboard will be saved as a csv file, if it already exists
it will append.all_model_outputs : The json path to the outputs of all models to add to the leaderboard (as a single file or by
globbing multiple files). Each dictionary should contain
the keys (instruction and output) that are formatted in the prompts and a column generator with the name of the
current model. As an example
see this file.reference_outputs the path to the outputs of the reference model. Each dictionary should contain
the keys (instruction and output) that are formatted in the prompts. By
default, the reference outputs are the 003 outputs on AlpacaEval set.annotators_config: The path to the annotator's config file. Defaults to alpaca_eval_gpt4.>>> alpaca_eval analyze_evaluators -- --helpNAME
alpaca_eval analyze_evaluators - Analyze an evaluator and populates the evaluators leaderboard (agreement with human, speed, price,...).
SYNOPSIS
alpaca_eval analyze_evaluators <flags>
DESCRIPTION
Analyze an evaluator and populates the evaluators leaderboard (agreement with human, speed, price,...).
FLAGS
--annotators_config=ANNOTATORS_CONFIG
Type: Union
Default: 'alpaca_eval_gpt4_turbo_fn'
The path the (or list of dict of) the annotator's config file.
-A, --Annotator=ANNOTATOR
Default: <class 'alpaca_eval.annotators.pairwise_evaluator.PairwiseAn...
The annotator class to use.
--analyzer_kwargs=ANALYZER_KWARGS
Type: Optional[Optional]
Default: None
Additional arguments to pass to the analyzer.
-p, --precomputed_leaderboard=PRECOMPUTED_LEADERBOARD
Type: Union
Default: PosixPath('/Users/yanndubois/Desktop/GitHub/alpaca_eval/src/...
The precomputed (meta)leaderboard of annotators or a path to it (json, csv, or tsv).
--is_save_leaderboard=IS_SAVE_LEADERBOARD
Type: bool
Default: False
Whether to save the leaderboard (ie analyzed results).
--is_return_instead_of_print=IS_RETURN_INSTEAD_OF_PRINT
Type: bool
Default: False
Whether to return the leaderboard (ie analyzed results). If True, it will not print the results.
--is_overwrite_leaderboard=IS_OVERWRITE_LEADERBOARD
Type: bool
Default: False
Whether to overwrite the leaderboard if it already exists.
-m, --max_instances=MAX_INSTANCES
Type: Optional[Optional]
Default: None
The maximum number of instances to analyze.
--is_single_annotator=IS_SINGLE_ANNOTATOR
Type: bool
Default: False
Whether to analyze a single annotator. If True, will not be able to estimate the annotator's bias.
-l, --leaderboard_mode_to_print=LEADERBOARD_MODE_TO_PRINT
Type: str
Default: 'minimal'
The mode of the leaderboard to print.
-c, --current_leaderboard_mode=CURRENT_LEADERBOARD_MODE
Type: str
Default: 'minimal'
The mode of the leaderboard to save all new entries with.
-o, --output_path=OUTPUT_PATH
Type: Union
Default: 'auto'
Path to save the leaderboard and annotataions. If None, we don't save.
Additional flags are accepted.
Additional arguments to pass to `Annotator`.
AlpacaEval provides a simple way of making new evaluators. All you need is to make a new configs.yaml configuration
file, which you will then pass
as --annotators_config <path_to_config.yaml> to alpaca_eval.
Here are some ways you can make a new evaluator:
prompt_template of the
configuration file. Paths are relative to the configuration file.completions_kwargs in the configuration file. To
see all available parameters refer to the docstrings of the corresponding
function in this file
specified by fn_completions
in the configuration file.model_name and the corresponding
prompt in prompt_template. If the model comes from another provider you
will
have
to change fn_completions which maps to the corresponding function
in this file. We
provide fn_completions functions to use models from OpenAI, Anthropic, Cohere, or HuggingFace. To
install packages needed for
all providers
use pip install alpaca_eval[all].The easiest is to check the docstrings
of SinglePairwiseAnnotator.
Here are some important ones:
Parameters
----------
prompt_template : path
A prompt that will be given to `fn_prompter` or path to the prompts. Path is relative to
`evaluators_configs/`
fn_completion_parser : callable or str
Function in `completion_parsers.py` to use for parsing the completions into preferences. For each completion,
the number of preferences should be equal to the batch_size if not we set all the preferences in that batch to
NaN.
completion_parser_kwargs : dict
Kwargs for fn_completion_parser.
fn_completions : callable or str
Function in `decoders.py` to use for decoding the output.
completions_kwargs : dict
kwargs for fn_completions. E.g. model_name, max_tokens, temperature, top_p, top_k, stop_seq.
is_randomize_output_order : bool
Whether to randomize output_1, output_2 when formatting.
batch_size : int
Number of examples that will be added in a single prompt.
Once you made the evaluator you can also analyze it and add it to the evaluator's leaderboard using the following command:
alpaca_eval analyze_evaluators --annotators_config '<path_to_config.yaml>'
To estimate the bias and variance this evaluates every example with 4 seeds, i.e., 2.5K
evaluation.
If you want a cheaper evaluation you can use a single seed using --is_single_annotator True which will skip the
estimation of bias and variance.
We are accepting PRs for new models, evaluators, and eval sets, in addition to bug fixes. We will update the leaderboard website regularly with new community contributions. We have also created a support discord for AlpacaEval in case you run into any issues and wish to ask help from the community.
To get started, please first fork the repo, and install the package from source pip install -e .
First, you'll need to add a model config definition in the models_configs folder. As an example, you can look at the falcon-7b-instruct yaml. Please make sure the folder name and key name in the yaml match exactly.
Then, please follow the steps in Evaluating a model to run inference on the model to produce outputs on the eval set and score the model according to one of the evaluators. An example command may look like:
alpaca_eval evaluate_from_model \
--model_configs 'falcon-7b-instruct'
After running this command, you should have generated an outputs json and a new entry in the corresponding leaderboard file. Please make a PR with the config, outputs file, and updated leaderboard.
Concretely you should do something like:
git clone <URL>src/alpaca_eval/models_configs/<model_name> and evaluate it evaluate_from_model --model_configs '<model_name>'git add src/alpaca_eval/models_configs/<model_name> # add the model config
git add src/alpaca_eval/leaderboards/ # add the actual leaderboard entry
git add src/alpaca_eval/metrics/weights # add the weights for LC
git add -f results/<model_name>/model_outputs.json # force add the outputs on the dataset
git add -f results/<model_name>/*/annotations.json # force add the evaluations from the annotators
git commit -m "Add <model_name> to AlpacaEval"
git push
Note: if you are generating outputs outside of AlpacaEval you should still add a model config but with fn_completions: null.
See this config for an example.
A verified result in AlpacaEval indicates that a core maintainer has decoded the outputs from the model and performed the evaluation. Unfortunately, we, the AlpacaEval maintainers, lack the resources to verify all the models and so we will only do that for models that are in the top-5 of the leaderboard. We apologize for any inconvenience this may cause and appreciate your understanding. To have your model verified, please follow the steps below:
@yann on Discord, or email us if you have our email, providing a brief rationale for why your model should be verified.alpaca_eval evaluate_from_model --model_configs '<your_model_name>' without requiring a local GPU.alpaca_eval evaluate_from_model --model_configs '<your_model_name>', update the results, and inform you so that you can revoke the temporary keys.Note that we will not re-evaluate the same model. Due to sampling variance, the results might slightly differ from your initial ones. We will replace your previous community results with the verified ones.
Please first follow the directions in Making a new evaluator. Once you're created the annotator config, we ask that you create a new leaderboard for the annotator by evaluating the minimal set of models. The outputs for these models can be found by downloading alpaca_eval_all_outputs.json.
alpaca_eval make_leaderboard \
--leaderboard_path src/alpaca_eval/leaderboards/data_AlpacaEval/<evaluator>_leaderboard.csv \
--all_model_outputs alpaca_eval_all_outputs.json \
--annotators_config <evaluator_config>
Then, please create a PR with the annotator config and leaderboard csv.
To contribute a new eval set, you'll first need to specify a set of textual instructions. Then, you'll need to specify a set of reference outputs (model win-rates are computed against this reference). For ease of use, you may use the default text-davinci-003 reference config.
Place these together into a json, where each entry specifies the fields instruction, output, and generator. You
can look to alpaca_eval.json as a
guide (the dataset field is not necessary).
Finally, we ask that you create a minimal leaderboard on this new evaluation set. You can do this with the following:
alpaca_eval make_leaderboard \
--leaderboard_path <src/alpaca_eval/leaderboards/data_AlpacaEval/your_leaderboard_name.csv> \
--all_model_outputs alpaca_eval_all_outputs.json \
--reference_outputs <path_to_json_file>
Please submit a PR with the eval set json and corresponding leaderboard csv.
Currently, we allow different completion functions, e.g., openai, anthropic, huggingface_local, huggingface_hub_api ... If you want to contribute a new completion function / API with which to perform inference then follow those steps:
<name>_completions(prompts : Sequence[str], model_name :str, ... ) in the decoder folder. This function should take as argument the prompts + kwargs and return the completions. Please look at other completion functions in the directory for templates. E.g. huggingface_local_completions or anthropic.<name>_completions and dependencies in init . Again you can follow the example of huggingface_local_completionsalpaca_eval evaluate_from_model --model_configs '<model_configs>'Feel free to start a PR early, we'll be able to provide some help in the process!
The AlpacaEval evaluation pipeline, like other current evaluators have important limitations and should therefore not be used as replacement for human evaluation in important settings, such as to decide whether a model is ready to be deployed. Those can broadly be clustered into 3 categories:
Instructions might not be representative of real-usage: the AlpacaEval set contains examples from a variety of datasets (self-instruct, open-assistant, vicuna, koala, hh-rlhf) which might not be representative of real-usage and advanced applications of better models like GPT4. This likely makes the best closed models (GPT4 / Claude / ChatGPT / ...) seem more similar to the open models than what they are. Indeed, those closed models seem to be pretrained/finetuned on much more diverse data. See for example this blog for preliminary results on more complex instructions. Note, however, that in AlpacaFarm we showed that win-rates on our evaluation set are highly correlated (0.97 R2) with win-rates on instructions from user interactions with the Alpaca Demo. Furthermore, the AlpacaEval leaderboard shows larger gap between the open models and OpenAI models than other leaderboards ( e.g. lmsys).
Biases of automatic annotators: the raw automatic annotators seem to have implicit biases. In particular, we found
that they tend to prefer longer outputs and outputs that contain lists (e.g. 0.68 / 0.69 for alpaca_eval_gpt4
and 0.62 / 0.58 for claude).
Although we found that humans have similar biases (0.64 / 0.61), we believe that this could be more of a limitation
of human annotation pipeline we used rather than a true human bias. More generally, through qualitative analysis, we
found that automatic annotators give more importance to the style
of the output than its content (e.g. factuality).
Finally, we found that automatic evaluators tend to prefer outputs from models that are similar (likely trained on
the same data) as suggested by the big difference between ChatGPT/GPT4 on claude's and alpaca_eval_gpt4's
leaderboard. Note that the length bias is partially mitigated in our length-controlled win-rates.
Lack of safety evaluation: importantly, AlpacaEval only evaluates the instruction-following capabilities of models rather than the harm that they could cause (e.g. toxic behavior or bias). As a result the small gap between current ChatGPT and the best open source models should not be interpreted as if that the latter are ready to be deployed.
Beyond those limitations about the evaluation pipelines, there are also limitations about our validation of the evaluators and our proposed approach to selecting evaluation sets.
First, our validation of evaluators based on human cross-annotations suffers from the following limitations: (1) we qualitatively found that our crowd-workers tend to also favor style such as length and presence of lists over factuality; (2) this does not validate whether win-rates against a reference model is a good evaluation strategy in the first place; (3) preferences from 16 crowd-workers are not representative of preferences of all humans.
Second, our suggested approach to selecting evaluation sets based on statistical power suffers from the following limitations: (1) statistical power does not ensure the right direction, e.g. you can have an unnatural set of instructions where Alpaca "performs" better than better model; and (2) this can push users to select data to support the hypothesis that they want to validate.
Length-controlled AlpacaEval Visualizations:
Length-controlled AlpacaEval Development:
The notebook shows different options that we considered for mitigating the length bias of automatic annotators.
Here we briefly summarize the main results. Namely:
win_rate(m,b) = 1 - win_rate(b,m) \in [0,1] and win_rate(m,m) = 0.5. This is shown in the plot below.
Finally, note that we are only controlling for length bias. There are other known biases that we are not controlling for, such as the fact that auto-annotators prefer outputs similar to their model. Although we could control for that, in practice we have found that to be less of an issue than length bias. For two reasons (1) this mostly a single model in the leaderboard because fine-tuning on outputs from the auto-annotator doesn't seem to have doesn't seem to impact the win-rate as much, and (2) the bias is actually less strong that what one could think. For example we show below a subset of the leaderboards auto-annotated by three different models, and we see that the ranking of models is exactly the same. In particular, claude-3-opus prefers gpt4_preview, and mistral-large prefers the former two.
Caution: all the following results are about AlpacaEval 1.0 and have not been updated since
Analyzing evaluators:
As we saw in the evaluator's leaderboard, there are many metrics to consider when selecting an evaluator, e.g. the quality, price, and speed. To assist with selection of the evaluator we provide a few functions to plot those metrics. The following shows for example the price/time/agreement of the different evaluators.
Here we see that alpaca_eval_gpt4 performs very well and is better than humans on all the considered metrics.
Previously we only considered the agreement with human annotators overall.
An additional validation that one could do is checking whether making a leaderboard using our
automatic annotator gives similar results as a leaderboard from humans.
To enable such analysis, we release human
annotations of outputs from 22 methods from AlpacaFarm =>
22*805 = ~18K annotations. As a result we
can
test
the correlation between the win-rates of the 22 models as evaluated by the humans and our automatic annotator.
Note that this is arguably a better way of selecting an automatic evaluator than using "human agreement [%]" but is
expensive given that it requires 18K
annotations.
The plot below shows such correlation for the alpaca_eval_gpt4 evaluator.
We see that the alpaca_eval_gpt4 leaderboard is highly correlated (0.94 Pearson correlation) to the leaderboard from
humans, which further
suggests that automatic evaluation is a good proxy for human evaluation.
For the code and more analysis,
see this notebook, or the
colab notebook above.
Caution: all the following results are about AlpacaEval 1.0 and have not been updated since.
Making evaluation sets:
When creating an evaluation set there are two main factors to consider: how much data to use? and what data?
One way of answering those question is by considering a leaderboard of models that you believe are of different quality and checking what and how much data is needed to distinguish between them in a statistically significant way. We will do so below using a paired t-test to test if the difference in win-rates between every pair of models is statistically significant.
First, let us consider the question of how much data to use.
Below we show the number of random samples needed from AlpacaEval for the paired t-test to give a p-value < 0.05 for
each pair of models in the minimal alpaca_eval_gpt4
leaderboard.
Grey cells correspond to pairs that are not significantly different on the 805 samples.
y- and x-axis are ordered by the win-rate of the first and second model respectively.
We see that most models can already be distinguished with 50 samples, and that 150 samples allows distinguishing the
majority of pairs (74 out of 78). This suggests that we can decrease the evaluation set size by a factor of
4 when testing two models that have similar performance gaps as those on the
minimal alpaca_eval_gpt4 leaderboard.
The second question is what data to use. Again we can try to answer this question from a statistical power perspective: what data allows to best distinguish between models. Let's consider this for all the datasets that are part of AlpacaEval, but let us control for the size of the evaluation sets as we only care about the quality of the data. The following plot shows the p-values from the paired t-test of each pairs of models on 80 examples of each subset of AlpacaEval.
We see for example that the self-instruct dataset yields the least statistical power, which suggests that one could remove this dataset from the evaluation set. The exact reason should be analyzed in future work. For the code and more analysis see this notebook, or the colab notebook above.
Please consider citing the following depending on what you are using and referring to:
alpaca_eval (this repo). Specify whether you are using AlpacaEval or AlpacaEval 2.0. For length-controlled win-rates see below.alpaca_eval_length.dubois2023alpacafarm (AlpacaFarm)alpaca_eval and self-instruct,
open-assistant, vicuna, koala, hh-rlhf.Here are the bibtex entries:
@misc{alpaca_eval,
author = {Xuechen Li and Tianyi Zhang and Yann Dubois and Rohan Taori and Ishaan Gulrajani and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto },
title = {AlpacaEval: An Automatic Evaluator of Instruction-following Models},
year = {2023},
month = {5},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/tatsu-lab/alpaca_eval}}
}
@article{dubois2024length,
title={Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators},
author={Dubois, Yann and Galambosi, Bal{\'a}zs and Liang, Percy and Hashimoto, Tatsunori B},
journal={arXiv preprint arXiv:2404.04475},
year={2024}
}
@misc{dubois2023alpacafarm,
title={AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback},
author={Yann Dubois and Xuechen Li and Rohan Taori and Tianyi Zhang and Ishaan Gulrajani and Jimmy Ba and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto},
year={2023},
eprint={2305.14387},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
Length controlled (LC) win-rates are a debiased version of the win-rates that control for the length of the outputs.
The main idea is that for each model we will fit a logistic regression to predict the preference of the autoannotator given: (1) the instruction, (2) the model, and (3) the difference of length between the baseline and model output.
Given such a logistic regression we can then try to predict the counterfactual "what would the preference be if the model's output had the same length as the baseline" by setting the length difference to 0.
By averaging over this length-controlled preference, we then obtain the length-controlled win-rate.
The exact form of the logistic regression is taken such that the interpretation of LC win rates is similar to the raw win rates, for example for any model m1 and m2 we have win_rate(m1, m2) = 1 - win_rate(m2, m1) \in [0,100] and win_rate(m1, m1) = 0.5.
Length controlled win-rates increase the correlation between AlpacaEval's leaderboard and Chat Arena from 0.93 to 0.98 Spearman correlation, while significantly decreasing the length gameability of the annotator.
For more information and results about length controlled win-rates see this notebook.
This idea of estimating the controlled direct effect, by predicting the outcome while conditioning on the mediator (the length difference), is common in statistical inference.
To get LC win rates on previously annotated models, you can use the following command:
pip install -U alpaca_eval
alpaca_eval --model_outputs … --is_recompute_metrics_only True
AlpacaEval 2.0 is a new version of AlpacaEval. Here are the differences:
gpt4_turbo: we upgraded the baseline from text-davinci-003 to gpt4_turbo to make the benchmark more challenging and have a metric that better reflects the current state of the art.weighted_alpaca_eval_gpt4_turbo: we improved the annotator in quality and price. First, we use the gpt4_turbo model for annotating, which is approximately 2x cheaper than gpt4. Second, we changed the prompt such that the model outputs a single token, which further reduced cost and speed. Finally, instead of using a binary preference, we used the logprobs to compute a continuous preference, which gives the final weighted win-rate. Note that the latter two changes had the surprising effect of decreasing the annotators' length biased.By default, AlpacaEval 2.0 will be used from pip install alpaca_eval==0.5. If you wish to use the old configs by default, you can set IS_ALPACA_EVAL_2=False in your environment.
As part of AlpacaEval, we release the following data:
text-davinci-003
reference on the AlpacaFarm evaluation set. Annotations are from a pool of 16 crowd workers on Amazon Mechanical Turk.
The different models are: 6 from OpenAI, 2 SFT models from AlpacaFarm, 13 RLHF methods from AlpacaFarm, and LLaMA 7B.For more details about the human annotations refer to the AlpacaFarm paper.
AlpacaEval is an improvement and simplification of the automatic pairwise preference simulator from AlpacaFarm. Outside AlpacaFarm, you should be using AlpacaEval. Here are the main differences:
{instruction}\n\n{input}. This affects 1/4 of the
examples in the AlpacaFarm evaluation set (the self-instruct subset).
This simplification provides a more fair comparison for models that were not trained by distinguishing between
the two fields.text-davinci-003),
so the results on AlpacaEval are not comparable to those on AlpacaFarm even for examples that had no input
field.--anotators_config 'alpaca_farm' and --p_label_flip 0.25 when creating an evaluator.There have been several work that propose new automatic annotators for instruction-following models. Here we list the ones that we are aware of and discuss how they differ from ours. We evaluated all of those in our evaluator's leaderboard.
lmsys_gpt4) evaluates the pair by asking the annotator a score from 1-10 for
each output, and then selecting the output with the highest score as preferred. They do not randomize over output
order and they ask an explanation after the score. Overall, we found that this annotator has strong bias towards
longer outputs (0.74) and relatively low correlation with human annotations (63.2).alpaca_farm_greedy_gpt4) evaluates the pair by directly asking the
annotator
which output it prefers. Furthermore, it batches 5 examples together to amortize the length of the prompt and
randomizes the order of outputs. Overall, we
found that this annotator has much less bias towards longer outputs (0.60) and is faster (878 seconds/1000 examples)
than others. It has a
slightly higher correlation with the majority of human annotations (66.4) than humans themselves (65.7).
However, it is more expensive ($15.3/1000 examples) and doesn't work with very long outputs given the batching.aviary_gpt4) asks the annotator to order the output by its preference, rather than
simply selecting the preferred output. It does not randomize the order of outputs and uses high temperature for
decoding (0.9). Overall, we found that this annotator has relatively strong bias towards longer outputs (0.70) and
very high
correlation with human annotations (69.1). By decreasing the temperature and randomizing the order of outputs,
we further improved
the correlation to 69.8 (improved_aviary_gpt4) but this further increased the length bias to 0.73.Our alpaca_eval_gpt4 is a mix between the AlpacaFarm and Aviary annotators. It asks the annotator to order the outputs
by preference, but it uses temperature 0, randomizes over outputs, and made some modifications to the prompt to decrease
length bias to 0.68.
Other related work include recent papers which analyze automatic evaluators. For example:
For all models you can find the auto-annotations under results/<model_name>/*/annotations.json. The annotations have the following columns:
instruction: the promptgenerator_1: the baseline modeloutput_1: the output of the baseline modelgenerator_2: the model being evaluatedoutput_2: the output of the model being evaluatedannotator: the auto-annotatorpreference: the result of the auto-annotator. This is a float between 1 and 2. Closer to 1 means that the auto-annotator prefers output_1, closer to 2 means that it prefers output_2. For AlpacaEval 2.0, preference-1 corresponds to the probability of output_1 being preferred. For AlpacaEval 1.0, preference is 1 if output_1 is preferred, 2 if output_2 is preferred, and 1.5 if they are the same. The win rate is always(preference -1).mean().raw_completion: the raw output of the auto-annotator. This is field contains the completions before de-randomization of the order between output_1 and output_2! It is thus much harder to interpret, see below for more information.Chain of thought
For some annotators, e.g. alpaca_eval_cot_gpt4_turbo_fn we use chain of thought reasoning to make the models preferences more interpretable. Those can then be found under concise_explanation. To interpret them, you should also look at referenced_models which translates the temporary model name (in the prompt) to the actual output. Below, we provide more explanation as to what is happening behind the scenes.
You can check the raw_annotations["concise_explanation] column in annotations.json (e.g. here) which contains the chain of thought reasoning of the auto annotator. Note that the raw_annotations is not modified by the randomization of the order of the outputs. In particular, "m" and "M" can sometime refer to the first model (the reference) and sometime to the second model (the model being evaluated). To understand which model is being referred to, you should use the column preference and ordered_models. To make it easier we add a column "referenced_models" mapping the model names to the corresponding outputs. For example in the following annotation we see that the preference is 1.0 (i.e. output_1) and corresponds to model M in concise_explanation (see ordered_models).
{
"instruction": "How did US states get their names?",
"output_1": "The names of U.S. states are derived from a variety of languages and sources, including indigenous American languages, European languages (primarily English, Spanish, and French), as well as other historical influences. Here is a brief overview of how some states got their names:\n\n1. **Indigenous American Languages**: Many state names come from Native American words or names of tribes, often from the languages of the indigenous peoples who lived in or near those areas.\n - **Alabama**: Possibly derived from the Choctaw language, meaning \"thicket clearers.\"\n - **Connecticut**: From a Mohegan-Pequot word meaning \"long tidal river.\"\n - **Massachusetts**: [...]",
"generator_1": "gpt4_1106_preview",
"dataset": "helpful_base",
"output_2": "The names of the 50 U.S. states come from a variety of sources, including Native American languages, European languages, and historical figures. Here's a brief overview of how some states got their names:\n\n1. Native American origins: Many states have names derived from Native American languages. For example, Alabama comes from the Choctaw word \"Albah amo,\" meaning \"plant gatherers\" or \"herb gatherers.\" Similarly, the name Mississippi comes from the Ojibwe word \"Misi-ziibi,\" meaning \"great river.\"\n\n2. European languages: [...].",
"generator_2": "gpt4",
"annotator": "alpaca_eval_cot_gpt4_turbo_fn",
"preference": 1.0,
"raw_completion": {
"concise_explanation": "Model M provided a more detailed and structured response, including bold headings for each category and a wider range of examples. It also included additional categories such as 'Other European Languages' and 'Combination of Languages and Influences', which added depth to the explanation. Model m's response was accurate but less comprehensive and lacked the clear structure found in Model M's output.",
"ordered_models": [
{
"model": "M",
"rank": 1
},
{
"model": "m",
"rank": 2
}
]
},
"referenced_models": {
"M": "output_1",
"m": "output_2"
}
}
chatgpt_fn that anyone can use (no waiting lists).chatgpt_fn
or alpaca_eval_gpt4_fn.FAQs
AlpacaEval : An Automatic Evaluator of Instruction-following Models
We found that alpaca-eval demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 3 open source maintainers collaborating on the project.
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