Package: alpaca-eval
AlpacaEval: An Automatic Evaluator for Instruction-following Language Models:tada:AlpacaEval 2.0is out and used by default! We improved the auto-annotator (better and cheaper) and use GPT-4 turbo as baseline. More detailshere. For the old version, set your environment variableIS_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 fundamentallimitationslike the preference for longer outputs.
AlpacaEval provides the following:Leaderboard: a leaderboard of common models on the AlpacaEval
evaluation set.Caution: Automatic evaluators (e.g. GPT-4) may be biased towards models that generate longer outputs and/or that were fine-tuned on the model underlying the evaluator (e.g. GPT-4).Automatic evaluator: an automatic evaluator that has high agreement with humans (validated on 20K
annotations). We evaluate a
model by
measuring the fraction of times a powerful LLM (e.g. GPT-4) prefers the outputs from that model
over
outputs from a reference model. Our evaluators enable caching and output randomization by default.Toolkit for building automatic evaluators: a simple interface for
building advanced automatic evaluators (e.g. with caching, batching, or multi-annotators) and analyzing them (quality,
price, speed, statistical power, bias, variance etc).Human evaluation data: 20K human preferences between a given and reference model
on theAlpacaFarmevaluation set. 2.5K of these are cross-annotations (4 humans annotating the same 650 examples).AlpacaEval dataset: a simplification
ofAlpacaFarm'sevaluation set, where "instructions" and "inputs" are merged into one field, and reference outputs are longer.Details here.When to use and not use AlpacaEval?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 shouldnot 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 inlimitations.Table of ContentsQuick StartLeaderboards and how to interpret themModelsEvaluatorsUse-casesEvaluating a modelMaking a new leaderboardMaking a new evaluatorContributingContributing a modelContributing an evaluatorContributing an eval setContributing a completion functionLimitationsAnalysisAnalyzing an evaluatorAnalyzing an eval setCitationAdditional informationAlpacaEval 2.0Data ReleaseDifferences with AlpacaFarmRelated workInterpreting annotationsMajor updatesQuick StartTo install the stable release, runpipinstallalpaca-evalTo install the nightly version, runpipinstallgit+https://github.com/tatsu-lab/alpaca_evalThen you can use it as follows:exportOPENAI_API_KEY=<your_api_key># for more complex configs, e.g. using Azure or switching clients see client_configs/README.mdalpaca_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 themodel_outputsfile. Important parameters are the following:model_outputs: A path to a json file for the outputs of the model to add to the leaderboard. Each dictionary
should
contain the keysinstructionandoutput.annotators_config: This is the annotator to use. We recommend usingweighted_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 seehere.reference_outputs:  The outputs of the reference model. Same format asmodel_outputs. By default, this
isgpt4_turbofor AlpacaEval 2.0.output_path: Path for saving annotations and leaderboard.If you don't have the model outputs, you can
useevaluate_from_modeland
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 usealpaca_eval <command> -- --help.Leaderboards and how to interpret themModelsOur leaderboards are computed on theAlpacaEval 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 foundon this page.
"AlpacaEval 2.0" usesweighted_alpaca_eval_gpt4_turbofor the annotator andgpt4_turbofor the baseline.
"AlpacaEval" usesalpaca_eval_gpt4for the annotator andtext_davinci_003for the baseline.
For all precomputed leaderboards seehere.
Later we also show how toadd your modelto the
leaderboard and how to make
anew leaderboard for your evaluator/dataset.
Seeherefor the configs of all
models that are available out of the box.AlpacaEval minimal leaderboard:Win RateStd Errorgpt495.30.7claude88.41.1chatgpt86.11.2guanaco-65b71.81.6vicuna-13b70.41.6text_davinci_00350.00.0alpaca-farm-ppo-human41.21.7alpaca-7b26.51.5text_davinci_00115.21.2How exactly are those metrics computed?Win Rate: the win rate measures the fraction of time the model's output is preferred over the reference's outputs (test-davinci-003for AlpacaEval andgpt4_turbofor 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.
SeeAlpacaEval'sandAlpacaEval 2.0'sprompts 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.Details about our auto-annotator:alpaca_eval_gpt4Ouralpaca_eval_gpt4(
seeconfigs)
annotator averages over preferences, where preferences are obtained as follows:it takes in an instruction and a pair of outputs (from the desired model and the reference model)if a preference was this triple was already computed, it returns it (i.e. it uses caching)it randomizes the order of the outputs to avoid position biasit formats the instruction and outputs into
thefollowing zero-shot prompt,
which asks to order the outputs in order of preferenceit completes the prompt using GPT4 withtemperature=0it parses the preference from the completions and returns itThe annotator is a mix between (and was highly influenced by)AlpacaFarmandAviaryevaluators.
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 inRelated work.For AlpacaEval 2.0 we useweighted_alpaca_eval_gpt4_turbo, which uses logprobs to compute continuous preference and uses GPT4_turbo as model (
seeconfigs).EvaluatorsWe evaluate different automatic annotators on the AlpacaEval set by comparing to
2.5Khuman annotationswe 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).
Seeherefor the configs of all
evaluators that are available out of the box and their associated metrics.Human agreementPrice [$/1000 examples]Time [seconds/1000 examples]Spearman corr.Pearson corr.BiasVarianceProba. prefer longeralpaca_eval_gpt469.213.614550.970.9328.414.60.68alpaca_eval_cot_gpt4_turbo_fn68.66.319890.970.9029.318.40.67alpaca_eval_gpt4_turbo_fn68.15.58640.930.8230.215.60.65gpt466.912.510370.880.8731.514.60.65alpaca_farm_greedy_gpt466.415.38780.850.7530.219.30.60alpaca_eval_cot_gpt4_turbo_fn65.74.32280.780.7733.923.70.61humans65.7300.0368001.001.000.034.30.64claude65.33.31730.930.9032.418.50.66lmsys_gpt465.313.9179820.980.9731.615.90.74text_davinci_00364.18.71210.850.8333.822.70.70longest62.20.000.270.5637.80.01.00chatgpt57.30.82850.720.7139.434.10.59How exactly are those metrics computed?We now explain in words how we compute the metrics in the table
above.The code is here.Human agreement: this measures the agreement between the current annotator and the majority preferences of
humans on
our
~650 annotations from
ourcross-annotation set,
which contains 4 human annotations per example.
To estimate the agreement between a single human (humansrow in the table above) and the majority of humans, we take
one of the 4 annotations and compute the accuracy that it has when predicting the mode of the other 3 annotations.
We then average this accuracy over all 4 annotations and over the 650 instructions to get the human agreement, i.e., we
compute the expected (over humans and samples)
leave-one-out agreement.
If the mode is not unique, we take one of the modes at random.
We perform exactly the same computation for the automatic annotators, so that the final numbers are comparable.Price [$/1000 examples]: this is the average price of every 1000 annotations.
For humans, it is the price thatwe paid Mechanical Turkersto 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 withHuman 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 withSpearman 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 thefull tablewe
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 aslmsysandaviary, 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.Tips for choosing evaluatorsOverall we recommend usingannotators_config=weighted_alpaca_eval_gpt4_turboif you want the high agreement with humans, andannotators_config=chatgpt_fnif 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
highperformance=62.2of
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 inAlpacaFarm.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 seehere.
In general, we foundweighted_alpaca_eval_gpt4_turboto 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 inlimitations.Use-casesEvaluating a model>>> 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 ARGUMENTSTo evaluate a model you need to:Choose an evaluation set and compute outputs specified asmodel_outputs. By default, we use
the 805 examples fromAlpacaEval. To compute outputs on AlpacaEval use:importdatasetseval_set=datasets.load_dataset("tatsu-lab/alpaca_eval","alpaca_eval")["eval"]forexampleineval_set:# generate here is a placeholder for your models generationsexample["output"]=generate(example["instruction"])if your model is a HuggingFace model or from a standard API provider (OpenAI, Anthropic, Cohere). Then you can
directly usealpaca_eval evaluate_from_modelto also take care of generating outputs.Compute the reference outputsreference_outputs. By default, we use precomputed outputs ofgpt4_turboon
AlpacaEval.
If you
want to use a different model or a different dataset follow the same steps as (1.).Choose an evaluator specified viaannotators_config. We recommend usingalpaca_eval_gpt4_turbo_fn. For other options and comparisons
seethis table. Depending on the evaluator you might need to
set the appropriate API_KEY in your environment
or int theclient_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 useevaluate_from_modelwhich takes care of decoding (model and reference)
for you.
Here's an
example:# need a GPU for local modelsalpaca_evalevaluate_from_model\--model_configs'oasst_pythia_12b'\--annotators_config'alpaca_eval_gpt4_turbo_fn'Here themodel_configsandreference_model_configs(optional) are paths to a directory that specifies the prompt,
the model
provider (here HuggingFace) and decoding parameters.
Seethis directoryfor examples.
For all model providers that are available out-of-the-box
seehere.Information about annotatorsCaching: by default all annotations are cached on
disk atcaching_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.Output randomizationby default, we randomize over the examples of outputs, as we found that annotators tend to
prefer the first examples
they see.Batchingwe provide code and examples to batch annotations, which decreases cost and time for annotations if the
prompt is long. See for
examplealpaca_farm_greedy_gpt4.Pool of annotatorswe provide code and examples to evaluate using a pool of automatic annotators, which is helpful
for replicating the variance ofhuman annotations. See for
examplealpaca_farm.Seeding based on instructionsFor reproducibility and more fair comparison between models, we seed all
randomness (output order, order in batches,
examples for each annotator in a pool) based on the instruction.Making a new leaderboard>>> 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 multiplealpaca_evalcalls), for your
desired evaluation
set and evaluators, you can use the following:alpaca_evalmake_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 (instructionandoutput) that are formatted in the prompts and a columngeneratorwith the name of the
current model. As an example
seethis file.reference_outputsthe path to the outputs of the reference model. Each dictionary should contain
the keys (instructionandoutput) 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 toalpaca_eval_gpt4.Making a new evaluator>>> 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 newconfigs.yamlconfiguration
file, which you will then pass
as--annotators_config <path_to_config.yaml>toalpaca_eval.
Here are some ways you can make a new evaluator:Changing the prompt: Write a new prompt in a text file and specify the path inprompt_templateof the
configuration file. Paths are relative to the configuration file.Changing decoding parameters: Specify the desired parameters incompletions_kwargsin the configuration file. To
see all available parameters refer to the docstrings of the corresponding
functionin this filespecified byfn_completionsin the configuration file.Changing the model: Specify the desired model inmodel_nameand the corresponding
prompt inprompt_template. If the model comes from another provider you
will
have
to changefn_completionswhich maps to the corresponding function
inthis file. We
providefn_completionsfunctions to use models from OpenAI, Anthropic, Cohere, or HuggingFace. To
install packages needed for
all providers
usepip install alpaca_eval[all].Other parameters in the configuration fileThe easiest is to check the docstrings
ofSinglePairwiseAnnotator.
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 theevaluator'sleaderboardusing the
following command:alpaca_evalanalyze_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 Truewhich will skip the
estimation of bias and variance.ContributingWe are accepting PRs for new models, evaluators, and eval sets, in addition to bug fixes.
We will update theleaderboard websiteregularly with new community
contributions.
We have also created asupport discordfor 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 sourcepip install -e .Contributing a modelFirst, you'll need to add a model config definition in themodels_configsfolder. As
an example, you can look at
thefalcon-7b-instruct yaml. Please make sure the
folder name and key name in the yaml match exactly.Then, please follow the steps inEvaluating a modelto 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_evalevaluate_from_model\--model_configs'falcon-7b-instruct'After running this command, you should have generated an outputs json and a new entry in the correspondingleaderboard
file. Please make a PR
with the
config, outputs file, and updated leaderboard.Concretely you should do something like:Fork the repository in githubClone the forked repositorygit clone <URL>Make a model config atsrc/alpaca_eval/models_configs/<model_name>and evaluate itevaluate_from_model --model_configs '<model_name>'Add the model configs, output, and leaderboard entry to the forked repositorygitaddsrc/alpaca_eval/models_configs/<model_name>
gitaddsrc/alpaca_eval/leaderboards/data_AlpacaEval
gitaddresults/<model_name>/model_outputs.json
gitaddresults/<model_name>/*/annotations.json
gitcommit-m"Add <model_name> to AlpacaEval"gitpushCreate apull request on AlpacaEvalNote: if you are generating outputs outside of AlpacaEval you should still add a model config but withfn_completions: null.
Seethis configfor an example.Getting your model verifiedA 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 (time/GPU/money) to verify all models. We apologize for any inconvenience this may cause and appreciate your understanding. To have your model verified, please follow the steps below:Contact@yannor@rtaorion Discord, or email us if you have our email, providing a brief rationale for why your model should be verified.Await our response and approval before proceeding.Prepare a script to decode from your model that does not require a GPU, typically the same script used for your model contribution. It should run usingalpaca_eval evaluate_from_model --model_configs '<your_model_name>'without requiring a local GPU.Generate temporary API keys for running the script and share them with us. Specifically, we need the keys for both decoding your model and for evaluation (e.g., OpenAI or Anthropic key).We will executealpaca_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.Contributing an evaluatorPlease first follow the directions inMaking 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
downloadingalpaca_eval_all_outputs.json.alpaca_evalmake_leaderboard\--leaderboard_pathsrc/alpaca_eval/leaderboards/data_AlpacaEval/<evaluator>_leaderboard.csv\--all_model_outputsalpaca_eval_all_outputs.json\--annotators_config<evaluator_config>Then, please create a PR with the annotator config and leaderboard csv.Contributing an eval setTo 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 defaulttext-davinci-003reference
config.Place these together into a json, where each entry specifies the fieldsinstruction,output, andgenerator. You
can look toalpaca_eval.jsonas a
guide (thedatasetfield 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_evalmake_leaderboard\--leaderboard_path<src/alpaca_eval/leaderboards/data_AlpacaEval/your_leaderboard_name.csv>\--all_model_outputsalpaca_eval_all_outputs.json\--reference_outputs<path_to_json_file>Please submit a PR with the eval set json and corresponding leaderboard csv.Contributing a completion functionCurrently, 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:add a file .py with a function<name>_completions(prompts : Sequence[str], model_name :str, ... )in thedecoder 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_completionsoranthropic.add<name>_completionsand dependencies ininit. Again you can follow the example ofhuggingface_local_completionsupdate optional dependencies insetup.pyadd a model you want to evaluate in themodels configsevaluate your model usingalpaca_eval evaluate_from_model --model_configs '<model_configs>'(optional) push the results from the previous model on AlpacaEval leaderboard followingthose stepsFeel free to start a PR early, we'll be able to provide some help in the process!LimitationsThe 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
examplethis blogfor preliminary results on more complex instructions.
Note, however, that inAlpacaFarmwe 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 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 foralpaca_eval_gpt4and 0.62 / 0.58 forclaude).
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 onclaude's andalpaca_eval_gpt4's
leaderboard.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 modelsshould notbe 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 ourproposed approachto selecting evaluation sets.Limitations about our validation pipelineFirst, 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.Additional analysis and plotsAlpacaEval provides a few visualization tools to help you analyze and improve your automatic evaluation pipeline. We
briefly explain
them here and provide
notebooks for more analysis.
For a description of all the metrics we consider
refer toHow exactly are those metrics computed?Analyzing an evaluatorAnalyzing evaluators:As we saw inthe 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 thatalpaca_eval_gpt4performs 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 releasehuman
annotationsof outputs from 22 methods fromAlpacaFarm=>
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 thealpaca_eval_gpt4evaluator.We see that thealpaca_eval_gpt4leaderboard 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,
seethis notebook, or the
colab notebook above.Analyzing an eval setMaking 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 minimalalpaca_eval_gpt4leaderboard.
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
minimalalpaca_eval_gpt4leaderboard.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
seethis notebook, or the
colab notebook above.CitationPlease consider citing the repo if you used the automatic annotators, code, or results.@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},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/tatsu-lab/alpaca_eval}}
}Make sure to specify whether you are using AlpacaEval or AlpacaEval 2.0.
If you used our human annotation data, please also consider citing theAlpacaFarmpaper:@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}
}If you use the AlpacaEval evaluation set, please cite each of the constituent
datasets:self-instruct,open-assistant,vicuna,koala,hh-rlhf.More informationAlpacaEval 2.0AlpacaEval 2.0 is a new version of AlpacaEval. Here are the differences:reference:gpt4_turbo: we upgraded the baseline fromtext-davinci-003togpt4_turboto make the benchmark more challenging and have a metric that better reflects the current state of the art.annotator:weighted_alpaca_eval_gpt4_turbo: we improved the annotator in quality and price. First, we use thegpt4_turbomodel for annotating, which is approximately 2x cheaper thangpt4. 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 frompip install alpaca_eval==0.5. If you wish to use the old configs by default, you can setIS_ALPACA_EVAL_2=Falsein your environment.Data ReleaseAs part of AlpacaEval, we release the following data:Human annotations (17701)in order to develop and understand automatic evaluators, we release all the human
pairwise
evaluation that we collected for AlpacaFarm. This contains comparisons between 22 models with thetext-davinci-003reference 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.Human cross-annotations (2596)in order to further analyze automatic evaluators we selected (via stratified
sampling
across models and datasets) 650 examples from the AlpacaFarm evaluation set and collected 4 human annotations per
example.AlpacaEval set (805)we made slight modifications/simplification of the AlpacaFarm evaluation set. In particular,
we first merged
the instruction and input fields into a single instruction field. This affects 1/4 of the examples in the AlpacaFarm
evaluation set, all of which are from theself-instruct evaluation set. Second we
regenerated the text-davinci-003 reference outputs without limiting the length of its outputs.For more details about the human annotations refer to theAlpacaFarm paper.Differences with AlpacaFarmAlpacaEval is an improvement and simplification of the automatic pairwise preference simulator
fromAlpacaFarm.
Outside AlpacaFarm, you should be using AlpacaEval.
Here are the main differences:AlpacaEval merges instructions and inputs: The AlpacaEval evaluation is the same as the AlpacaFarm evaluation
except that the instruction and input fields are merged as{instruction}\n\n{input}. This affects 1/4 of the
examples in the AlpacaFarm evaluation set (theself-instructsubset).
This simplification provides a more fair comparison for models that were not trained by distinguishing between
the two fields.AlpacaEval handles longer generations: Models in AlpacaFarm were limited to a maximum number of 300 tokens for
generations. We
change this number to 2000 for AlpacaEval. Note that this also affects the reference generations (text-davinci-003),
so the results on AlpacaEval are not comparable to those on AlpacaFarm even for examples that had no input
field.AlpacaEval removes intra- and inter-annotator variance: The AlpacaFarm simulator replicates human annotation in
terms of both mode behavior and diversity.
In particular, AlpacaFarm's simulator uses a pool of models and prompts and adds noise to replicate human intra- and
inter-annotator variance.
If the goal is to use an automatic annotator for evaluation or simply training better models, then this variance
may not be desirable. The default annotators in AlpacaEval thus don't have this variance. We give the option to add it
back by
using--anotators_config 'alpaca_farm'and--p_label_flip 0.25when creating an evaluator.Related workThere 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
inour evaluator's leaderboard.Vicuna/lmsysThe lmsys annotator (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 explanationafterthe score. Overall, we found that this annotator has strong bias towards
longer outputs (0.74) and relatively low correlation with human annotations (63.2).AlpacaFarmThe best AlpacaFarm annotator (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.AviaryThe Aviary annotator (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,
wefurther improvedthe correlation to 69.8 (improved_aviary_gpt4) but this further increased the length bias to 0.73.Ouralpaca_eval_gpt4is 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:AlpacaFarm Appx CandLarge Language Models are not Fair Evaluatorsboth found that automatic
annotators have
a position bias.AlpacaFarm Sec. 5.2.andThe False Promise of Imitating Proprietary LLMsboth found that
automatic
annotators favor style (e.g. use of list, tone, word choice, length) over factuality.Interpreting annotationsFor all models you can find the auto-annotations underresults/<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 prefersoutput_1, closer to 2 means that it prefersoutput_2. For AlpacaEval 2.0,preference-1corresponds to the probability ofoutput_1being preferred. For AlpacaEval 1.0,preferenceis 1 ifoutput_1is preferred, 2 ifoutput_2is 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.Chain of throughFor some annotators, e.g.alpaca_eval_cot_gpt4_turbo_fnwe usechain of thought reasoningto make the models preferences more interpretable. Those can then be found underconcise_explanation. To interpret them, you should also look atreferenced_modelswhich 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 theraw_annotations["concise_explanation]column inannotations.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 columnpreferenceandordered_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 modelMinconcise_explanation(seeordered_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"}}Major updates3rd January 2024: updated to AlpacaEval 2.0, which uses GPT4-turbo as baseline and annotator.2nd January 2024: added Azure API and more general way of setting client configs. Seehere19th June 2023: add leaderboardchatgpt_fnthat anyone can use (no waiting lists).19th June 2023: update to
useOpenAI's function calling.
Example:chatgpt_fnoralpaca_eval_gpt4_fn.