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ZeroEval: A Unified Framework for Evaluating Language Models

ZeroEval is a simple unified framework for evaluating (large) language models on various tasks. This repository aims to evaluate instruction-tuned LLMs for their zero-shot performance on various reasoning tasks such as MMLU and GSM. We evaluate LLMs with a unified setup by controlling the factors such as prompting, sampling, output parsing, etc. In ZeroEval, we perform zero-shot prompting, and instruct LM to output both reasoning and answer in a json-formatted output. We are actively adding new tasks. Contributions are welcome!

Leaderboard: https://hf.co/spaces/allenai/ZeroEval
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Todo

Support new tasks (GPPA, AIME, etc.)
Prefix-prefill for open models such that the parsing is easier
Add other formatting options (e.g. markup language instead of json, etc.)

Installation

<details> <summary> Click to expand </summary>

conda create -n zeroeval python=3.10
conda activate zeroeval
# pip install vllm -U # pip install -e vllm 
pip install vllm -U
pip install -r requirements.txt
# export HF_HOME=/path/to/your/custom/cache_dir/

</details>

Tasks

MMLU-redux (-d mmlu-redux)
ZebraLogic (-d zebra-grid)
CRUX (-d crux)
MATH (Level 5) (-d math-l5)
GSM8K (-d gsm)
More tasks will be added soon. (e.g., ARC, MMLU-Pro, etc.)

Usage

zero_eval_local.sh and zero_eval_api.sh are the two main scripts to run the evaluation.

Examples

bash zero_eval_local.sh -d mmlu-redux -m meta-llama/Meta-Llama-3-8B-Instruct -p Meta-Llama-3-8B-Instruct -s 4 (Run Llama-3-8B-Instruct with greedy decoding on mmlu-redux)
bash zero_eval_api.sh -d gsm -f openai -m openai/gpt-4o-mini-2024-07-18 -p gpt-4o-mini-2024-07-18 -s 8 (Run gpt-4o-mini with greedy decoding on gsm)
bash zero_eval_api.sh -d zebra-grid -f openai -m deepseek-chat -p deepseek-chat -s 8 (Run deepseek-chat via openai style api, with greedy decoding on zebra-grid)

More examples can be found in the scripts folder, e.g., the scripts/_MMLU_redux.md and scripts/_GSM.md files as well as scripts/local/crux.sh.

Arguments

<details> <summary>Command Line Arguments</summary>

Arguments	Description	Default
`-d`	DATA_NAME: `mmlu-redux`, `gsm`, `math-l5`, `zebra-grid`, `alpaca_eval`, ... (see src/task_configs.py)
`-m`	model_name
`-p`	model_pretty_name
`-s`	number of shards (When `-s 1` we'll use all your GPUs for loading the model and running the inference; When `-s K`, we'll use K GPUs and divide the data into K shards for each GPU to run the inference on a single shard, and merge the results at the end.)	1
`-f`	engine (`vllm` by default for `zero_eval_local.sh`, can be changed to `hf`; For `zero_eval_api.sh`, we can use `openai`, `anthropic`, ...)	`vllm`/`openai` for `zero_eval_local/api.sh`
`-r`	run_name (the results will be saved in a sub folder with the `run_name` when it is specified)	"default"
`-t`	temperature	0 (greedy decoding)
`-o`	top_p for nucleus sampling	1.0
`-e`	repetition penalty	1.0
`-b`	batch size	4
`-x`	max_length	4096

</details>

Results

🚨 View results on our Leaderboard: https://hf.co/spaces/allenai/ZeroEval

MMLU-Redux: python src/evaluation/mcqa_eval.py mmlu-redux --> Full results
GSM/MATH-L5: python src/evaluation/math_eval.py math-l5/gsm --> Full results
ZebraLogic: python src/evaluation/zebra_grid_eval.py --> Full results and Leaderboard
CRUX: python src/evaluation/crux_eval.py --> Full results
All: python src/evaluation/summarize.py --> Full results ⬇️

Citation

If you find ZeroEval useful, please cite it as follows in your publication:

@software{Lin_ZeroEval_A_Unified_2024,
    author = {Lin, Bill Yuchen},
    month = jul,
    title = {{ZeroEval: A Unified Framework for Evaluating Language Models}},
    url = {https://github.com/WildEval/ZeroEval},
    year = {2024}
}

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