aviary.litqa 0.18.3

LitQA environment implemented with aviary

aviary.litqa

LitQA2 environment implemented with aviary, allowing agents to perform question answering on the LitQA dataset.

LitQA (now legacy) is a dataset composed from 50 multiple-choice questions from recent literature. It is designed to test the LLM's the ability to retrieve information outside of the pre-training corpus. To ensure the questions are not in the pre-training corpus, the questions were collected from scientific papers published after September 2021 -- cut-off date of GPT-4's training data.

LitQA2 is part of the LAB-Bench dataset. LitQA2 contains 248 multiple-choice questions from the literature and was created ensuring that the questions cannot be answered by recalling from the pre-training corpus only. It considered scientific paper published within 36 months from the data of its publication. Therefore, LitQA2 is considered a scientific RAG dataset.

Installation

To install the LitQA environment, run:

pip install fhaviary[litqa]

Usage

In litqa/env.py, you will find:

GradablePaperQAEnvironment: an environment that can grade answers given an evaluation function.

And in litqa/task.py, you will find:

LitQAv2TaskDataset: a task dataset designed to pull LitQA v2 from Hugging Face, and create one GradablePaperQAEnvironment per question

Here is an example of how to use them:

import os

from aviary.env import TaskDataset
from ldp.agent import SimpleAgent
from ldp.alg import Evaluator, EvaluatorConfig, MeanMetricsCallback

from paperqa import Settings
from litqa import TASK_DATASET_NAME


async def evaluate(folder_of_litqa_v2_papers: str | os.PathLike) -> None:
    settings = Settings(paper_directory=folder_of_litqa_v2_papers)
    dataset = TaskDataset.from_name(TASK_DATASET_NAME, settings=settings)
    metrics_callback = MeanMetricsCallback(eval_dataset=dataset)

    evaluator = Evaluator(
        config=EvaluatorConfig(batch_size=3),
        agent=SimpleAgent(),
        dataset=dataset,
        callbacks=[metrics_callback],
    )
    await evaluator.evaluate()

    print(metrics_callback.eval_means)

References

[1] Lála et al. PaperQA: Retrieval-Augmented Generative Agent for Scientific Research. ArXiv:2312.07559, 2023.

[2] Skarlinski et al. Language agents achieve superhuman synthesis of scientific knowledge. ArXiv:2409.13740, 2024.

[3] Laurent et al. LAB-Bench: Measuring Capabilities of Language Models for Biology Research. ArXiv:2407.10362, 2024.