BioGraphletQA: Knowledge-Anchored Generation of Complex QA Datasets

2026-04-30 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Biomedical Informatics · Depth: Expert, extended

Summary

A new framework, BioGraphletQA, systematically generates complex biomedical Question Answering (QA) data by anchoring Large Language Model (LLM) generation to graphlets from Knowledge Graphs (KGs). This framework addresses LLM hallucination and the limitations of traditional KGQA datasets by using small subgraphs from the OREGANO KG to guide question generation, ensuring factual grounding and linguistic complexity. The initial instantiation, BioGraphletQA, comprises 119,856 QA pairs, each grounded in 3-5 node graphlets from OREGANO KG v2.1 and enriched with PubMed document snippets. Expert evaluation of 106 QA pairs confirmed high scientific validity and complexity. Furthermore, augmenting downstream benchmarks with BioGraphletQA data improved PubMedQA accuracy from 49.2% to 68.5% in a low-resource setting and MedQA accuracy from 41.4% to 44.8% in a full-resource setting. The framework and dataset are publicly available.

Key takeaway

For research scientists developing biomedical QA systems, this framework offers a scalable solution to data scarcity and LLM hallucination. You should consider integrating graphlet-anchored generation and automated filtering into your data creation pipelines to produce high-quality, domain-specific datasets. This approach can significantly boost model performance, especially in low-resource settings, by providing factually grounded and complex training examples.

Key insights

Graphlet-anchored LLM generation creates complex, factually grounded QA datasets, mitigating hallucination in specialized domains.

Principles

• Factual grounding improves LLM QA reliability.
• Graphlets control complexity and ensure accuracy.
• Automated filtering enhances synthetic data quality.

Method

The framework involves KG pre-processing, graphlet extraction, modular prompt-guided LLM generation, automated filtering, supporting document retrieval from PubMed, and task-specific rephrasing for benchmark alignment.

In practice

• Augment training data with 10,000 synthetic samples.
• Use graphlets to constrain LLM output for factual accuracy.
• Implement LLM-as-a-judge for post-generation data filtering.

Topics

• Biomedical QA
• Knowledge Graph Question Answering
• Graphlet-Anchored Generation
• BioGraphletQA Dataset
• Large Language Models

Code references

• ieeta-pt/BioGraphletQA

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.