metadata

language:
  - en
license: mit
task_categories:
  - question-answering
  - text-retrieval
  - other
pretty_name: BioKGBench
size_categories: 1G<n<100G
annotations_creators:
  - expert-generated
  - machine-generated
task_ids:
  - fact-checking
  - closed-domain-qa
  - fact-checking-retrieval
dataset_info:
  features:
    - name: kgcheck
      dtype: string
    - name: kgqa
      dtype: string
    - name: scv
      dtype: string
    - name: bioKG
      dtype: string
configs:
  - config_name: kgcheck
    data_files:
      - split: dev
        path: kgcheck/dev.json
      - split: test
        path: kgcheck/test.json
  - config_name: kgqa
    data_files:
      - split: dev
        path: kgqa/dev.json
      - split: test
        path: kgqa/test.json
  - config_name: scv
    data_files:
      - split: corpus
        path: scv/merged_corpus.jsonl
      - split: dev
        path: scv/dev.jsonl
      - split: test
        path: scv/test.jsonl
  - config_name: biokg
    data_files:
      - split: data
        path: bioKG/*/*.tsv
tags:
  - agent
  - medical

Agent4S-BioKG

A Knowledge Graph Checking Benchmark of AI Agent for Biomedical Science.

Github

Introduction

Pursuing artificial intelligence for biomedical science, a.k.a. AI Scientist, draws increasing attention, where one common approach is to build a copilot agent driven by Large Language Models(LLMs).
However, to evaluate such systems, people either rely on direct Question-Answering(QA) to the LLM itself, or in a biomedical experimental manner. How to precisely benchmark biomedical agents from an AI Scientist perspective remains largely unexplored. To this end, we draw inspiration from one most important abilities of scientists, understanding the literature, and introduce BioKGBench.
In contrast to traditional evaluation benchmark that only focuses on factual QA, where the LLMs are known to have hallucination issues, we first disentangle Understanding Literature into two atomic abilities, i) Understanding the unstructured text from research papers by performing scientific claim verification, and ii) Ability to interact with structured Knowledge-Graph Question-Answering~(KGQA) as a form of Literature grounding. We then formulate a novel agent task, dubbed KGCheck, using KGQA and domain-based Retrieval-Augmented Generation (RAG) to identify the factual errors of existing large-scale knowledge graph databases. We collect over two thousand data for two atomic tasks and 225 high-quality annotated data for the agent task. Surprisingly, we discover that state-of-the-art agents, both daily scenarios and biomedical ones, have either failed or inferior performance on our benchmark. We then introduce a simple yet effective baseline, dubbed BKGAgent. On the widely used popular dataset, we discover over 90 factual errors which yield the effectiveness of our approach, yields substantial value for both the research community or practitioners in the biomedical domain.

Overview

Tasks

KGCheck: Given a knowledge graph and a scientific claim, the agent needs to check whether the claim is supported by the knowledge graph. The agent can interact with the knowledge graph by asking questions and receiving answers.
KGQA: Given a knowledge graph and a question, the agent needs to answer the question based on the knowledge graph.
SCV: Given a scientific claim and a research paper, the agent needs to check whether the claim is supported by the research paper.

Code Structure

Baseline

Dataset

News and Updates

[2024-06-06] BioKGBench v0.1.0 is released.

Installation

This project has provided an environment setting file of conda, users can easily reproduce the environment by the following commands:

conda create -n agent4s-biokg python=3.10
conda activate agent4s-biokg
pip install -r requirements.txt

Getting Started

Obtaining dataset: The dataset can be found in the [release]. The dataset is divided into three parts: KGCheck, KGQA, and SCV, every part is split into Dev and Test.

Running Baseline:

KGCheck:

KGQA:

SCV:

Acknowledgement

BioKGBench is an open-source project for Agent evaluation created by researchers in Westlake Auto Lab and CAIRI Lab. We encourage researchers interested in LLM Agent and other related fields to contribute to this project!

Citation

Contact

For adding new features, looking for helps, or reporting bugs associated with BioKGBench, please open a GitHub issue and pull request with the tag new features, help wanted, or enhancement. Feel free to contact us through email if you have any questions.

Xinna Lin(linxinna@westlake.edu.cn), Westlake University
Siqi Ma(masiqi@westlake.edu.cn), Westlake University
Junjie Shan(shanjunjie@westlake.edu.cn), Westlake University
Xiaojing Zhang(zhangxiaojing@westlake.edu.cn), Westlake University

TODO

Update dataset
Support pip installation