QA-Evaluation-Metrics 📊

A fast and lightweight Python package for evaluating question-answering models and prompting of black-box and open-source large language models.

pip install qa-metrics is all you need!

🎉 Latest Updates

Version 0.2.19 Released!
- Paper accepted to EMNLP 2024 Findings! 🎓
- Enhanced PEDANTS with multi-pipeline support and improved edge case handling
- Added support for OpenAI GPT-series and Claude Series models (OpenAI version > 1.0)
- Integrated support for open-source models (LLaMA-2-70B-chat, LLaVA-1.5, etc.) via deepinfra
- Introduced trained tiny-bert for QA evaluation (18MB model size)
- Added direct Huggingface model download support for TransformerMatcher

Prerequisites

Python >= 3.6
openai >= 1.0

Installation

pip install qa-metrics

💡 Features

Our package offers six QA evaluation methods with varying strengths:

Method	Best For	Cost	Correlation with Human Judgment
Normalized Exact Match	Short-form QA (NQ-OPEN, HotpotQA, etc.)	Free	Good
PEDANTS	Both short & medium-form QA	Free	Very High
Neural Evaluation	Both short & long-form QA	Free	High
Open Source LLM Evaluation	All QA types	Free	High
Black-box LLM Evaluation	All QA types	Paid	Highest

📖 Documentation

1. Normalized Exact Match

Method: `em_match`

Parameters

reference_answer (list of str): A list of gold (correct) answers to the question
candidate_answer (str): The answer provided by a candidate that needs to be evaluated

Returns

boolean: True if there are any exact normalized matches between gold and candidate answers

from qa_metrics.em import em_match

reference_answer = ["The Frog Prince", "The Princess and the Frog"]
candidate_answer = "The movie \"The Princess and the Frog\" is loosely based off the Brother Grimm's \"Iron Henry\""
match_result = em_match(reference_answer, candidate_answer)

2. F1 Score

Method: `f1_score_with_precision_recall`

Parameters

reference_answer (str): A gold (correct) answer to the question
candidate_answer (str): The answer provided by a candidate that needs to be evaluated

Returns

dictionary: Contains the F1 score, precision, and recall between a gold and candidate answer

Method: `f1_match`

Parameters

reference_answer (list of str): List of gold answers
candidate_answer (str): Candidate answer to evaluate
threshold (float): F1 score threshold for considering a match (default: 0.5)

Returns

boolean: True if F1 score exceeds threshold for any gold answer

from qa_metrics.f1 import f1_match, f1_score_with_precision_recall

f1_stats = f1_score_with_precision_recall(reference_answer[0], candidate_answer)
match_result = f1_match(reference_answer, candidate_answer, threshold=0.5)

3. PEDANTS

Method: `get_score`

Parameters

reference_answer (str): A Gold answer
candidate_answer (str): Candidate answer to evaluate
question (str): The question being evaluated

Returns

float: The similarity score between two strings (0 to 1)

Method: `get_highest_score`

Parameters

reference_answer (list of str): List of gold answers
candidate_answer (str): Candidate answer to evaluate
question (str): The question being evaluated

Returns

dictionary: Contains the gold answer and candidate answer pair with highest matching score

Method: `get_scores`

Parameters

reference_answer (list of str): List of gold answers
candidate_answer (str): Candidate answer to evaluate
question (str): The question being evaluated

Returns

dictionary: Contains matching scores for all gold answer and candidate answer pairs

Method: `evaluate`

Parameters

reference_answer (list of str): List of gold answers
candidate_answer (str): Candidate answer to evaluate
question (str): The question being evaluated

Returns

boolean: True if candidate answer matches any gold answer

Method: `get_question_type`

Parameters

reference_answer (list of str): List of gold answers
question (str): The question being evaluated

Returns

list: The type of the question (what, who, when, how, why, which, where)

Method: `get_judgement_type`

Parameters

reference_answer (list of str): List of gold answers
candidate_answer (str): Candidate answer to evaluate
question (str): The question being evaluated

Returns

list: A list revised rules applicable to judge answer correctness

from qa_metrics.pedant import PEDANT

pedant = PEDANT()
scores = pedant.get_scores(reference_answer, candidate_answer, question)
match_result = pedant.evaluate(reference_answer, candidate_answer, question)

4. Transformer Neural Evaluation

Method: `get_score`

Parameters

reference_answer (str): A Gold answer
candidate_answer (str): Candidate answer to evaluate
question (str): The question being evaluated

Returns

float: The similarity score between two strings (0 to 1)

Method: `get_highest_score`

Parameters

reference_answer (list of str): List of gold answers
candidate_answer (str): Candidate answer to evaluate
question (str): The question being evaluated

Returns

dictionary: Contains the gold answer and candidate answer pair with highest matching score

Method: `get_scores`

Parameters

reference_answer (list of str): List of gold answers
candidate_answer (str): Candidate answer to evaluate
question (str): The question being evaluated

Returns

dictionary: Contains matching scores for all gold answer and candidate answer pairs

Method: `transformer_match`

Parameters

reference_answer (list of str): List of gold answers
candidate_answer (str): Candidate answer to evaluate
question (str): The question being evaluated

Returns

boolean: True if transformer model considers candidate answer equivalent to any gold answer

from qa_metrics.transformerMatcher import TransformerMatcher

### supports `zli12321/roberta-large-qa-evaluator`, `zli12321/answer_equivalence_bert`, `zli12321/answer_equivalence_distilbert`, `zli12321/answer_equivalence_roberta`, `zli12321/answer_equivalence_distilroberta`
tm = TransformerMatcher("zli12321/answer_equivalence_tiny_bert")
match_result = tm.transformer_match(reference_answer, candidate_answer, question)

5. LLM Integration

Method: `prompt_gpt`

Parameters

prompt (str): The input prompt text
model_engine (str): OpenAI model to use (e.g., 'gpt-3.5-turbo')
temperature (float): Controls randomness (0-1)
max_tokens (int): Maximum tokens in response

from qa_metrics.prompt_llm import CloseLLM

model = CloseLLM()
model.set_openai_api_key(YOUR_OPENAI_KEY)
result = model.prompt_gpt(prompt=prompt, model_engine='gpt-3.5-turbo')

Method: `prompt_claude`

Parameters

prompt (str): The input prompt text
model_engine (str): Claude model to use
anthropic_version (str): API version
max_tokens_to_sample (int): Maximum tokens in response
temperature (float): Controls randomness (0-1)

model = CloseLLM()
model.set_anthropic_api_key(YOUR_ANTHROPIC_KEY)
result = model.prompt_claude(prompt=prompt, model_engine='claude-v1')

Method: `prompt`

Parameters

message (str): The input message text
model_engine (str): Model to use
temperature (float): Controls randomness (0-1)
max_tokens (int): Maximum tokens in response

from qa_metrics.prompt_open_llm import OpenLLM

model = OpenLLM()
model.set_deepinfra_key(YOUR_DEEPINFRA_KEY)
result = model.prompt(message=prompt, model_engine='mistralai/Mixtral-8x7B-Instruct-v0.1')

🤗 Model Hub

Our fine-tuned models are available on Huggingface:

📚 Resources

📄 Citation

@inproceedings{li-etal-2024-pedants,
    title = "{PEDANTS}: Cheap but Effective and Interpretable Answer Equivalence",
    author = "Li, Zongxia  and
      Mondal, Ishani  and
      Nghiem, Huy  and
      Liang, Yijun  and
      Boyd-Graber, Jordan Lee",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.findings-emnlp.548/",
    doi = "10.18653/v1/2024.findings-emnlp.548",
    pages = "9373--9398",
    abstract = "Question answering (QA) can only make progress if we know if an answer is correct, but current answer correctness (AC) metrics struggle with verbose, free-form answers from large language models (LLMs). There are two challenges with current short-form QA evaluations: a lack of diverse styles of evaluation data and an over-reliance on expensive and slow LLMs. LLM-based scorers correlate better with humans, but this expensive task has only been tested on limited QA datasets. We rectify these issues by providing rubrics and datasets for evaluating machine QA adopted from the Trivia community. We also propose an efficient, and interpretable QA evaluation that is more stable than an exact match and neural methods (BERTScore)."
}

📝 License

This project is licensed under the MIT License.

📬 Contact

For questions or comments, please contact: zli12321@umd.edu

zli12321/answer_equivalence_bert

QA-Evaluation-Metrics 📊

🎉 Latest Updates

🚀 Quick Start

Table of Contents

Prerequisites

Installation

💡 Features

📖 Documentation

1. Normalized Exact Match

Method: em_match

2. F1 Score

Method: f1_score_with_precision_recall

Method: f1_match

3. PEDANTS

Method: get_score

Method: get_highest_score

Method: get_scores

Method: evaluate

Method: get_question_type

Method: get_judgement_type

4. Transformer Neural Evaluation

Method: get_score

Method: get_highest_score

Method: get_scores

Method: transformer_match

5. LLM Integration

Method: prompt_gpt

Method: prompt_claude

Method: prompt

🤗 Model Hub

📚 Resources

📄 Citation

📝 License

📬 Contact

Method: `em_match`

Method: `f1_score_with_precision_recall`

Method: `f1_match`

Method: `get_score`

Method: `get_highest_score`

Method: `get_scores`

Method: `evaluate`

Method: `get_question_type`

Method: `get_judgement_type`

Method: `get_score`

Method: `get_highest_score`

Method: `get_scores`

Method: `transformer_match`

Method: `prompt_gpt`

Method: `prompt_claude`

Method: `prompt`