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Source note3 min read

From Voting to Agent Collaboration: Answer-Type-Aware LLM Pipelines for BioASQ 14b

Source

arXiv

arxiv.org/abs/2607.06452v1

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Plain-Language Brief

A study presents a framework for improving the accuracy and reliability of biomedical question answering by using a large language model (LLM) that adapts its inference strategies based on the type of question (yes/no, factoid, or list). The LLM uses different methods such as snippet shuffling, self-reflection, full-snippet input, chain-of-thought-based in-context learning, and a multi-agent architecture to handle each question type effectively. The framework was evaluated in the official BioASQ 14b Task B challenge and showed competitive performance across multiple batches, even achieving first place in the factoid subtask of Batch 4.

What the Document Says

  • The study introduces a question-type-specific LLM framework for improving answer robustness and evidence grounding in biomedical question answering.
  • Different inference procedures are selected based on the distinct reasoning requirements of yes/no, factoid, and list questions.
  • Snippet shuffling and self-reflection are used for yes/no questions to reduce sensitivity to evidence ordering and improve decision stability.
  • Full-snippet input combined with chain-of-thought-based in-context learning is used for factoid questions to support accurate biomedical entity identification.
  • A multi-agent architecture is employed for list questions, with evidence extraction, candidate generation, answer verification, and final aggregation handled collaboratively.
  • Preliminary experiments were conducted on BioASQ 13b to identify effective inference strategies for each question type.
  • The framework was evaluated in the official BioASQ 14b Task B challenge and showed competitive performance across multiple batches.
  • The factoid subtask of Batch 4 was won by the framework.

Key Concepts Mentioned

  • biomedical question answering
  • large language model (LLM)
  • snippet shuffling
  • self-reflection
  • full-snippet input
  • chain-of-thought-based in-context learning
  • multi-agent architecture
  • BioASQ 13b
  • BioASQ 14b Task B
  • competitive performance

Claims Made by the Document

  • The framework improves answer robustness and evidence grounding in biomedical question answering.
  • Different inference strategies are effective for yes/no, factoid, and list questions.

Evidence Used

  • The study presents a question-type-specific LLM framework that selects different inference procedures based on the distinct reasoning requirements of each question type.
  • The framework was evaluated in the official BioASQ 14b Task B challenge, showing competitive performance across multiple batches.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

Biomedical question answering requires not only accurate extraction of information from scientific literature but also reliable integration of evidence across multiple documents. This study presents a question-type-specific large language model (LLM) framework for BioASQ 14b Task B, designed to improve answer robustness and evidence grounding in biomedical question answering. Rather than applying a single prompting strategy to all questions, the framework selects different inference procedures for yes/no, factoid, and list questions according to their distinct reasoning and evaluation requirements. For yes/no questions, snippet shuffling and self-reflection are used to reduce sensitivity to evidence ordering and improve decision stability. For factoid questions, full-snippet input is combined with chain-of-thought-based in-context learning to support accurate biomedical entity identification. For list questions, a multi-agent architecture is employed, in which evidence extraction, candidate generation, answer verification, and final aggregation are handled collaboratively. Preliminary experiments on BioASQ 13b were used to identify effective inference strategies for each question type, and the resulting framework was subsequently evaluated in the official BioASQ 14b Task B challenge. In the official evaluation, our framework showed competitive performance across multiple batches and achieved first place in the factoid subtask of Batch 4. These results demonstrate the effectiveness of combining question-type-specific inference, ensemble prediction, and agent-based verification for reliable biomedical question answering.