- The paper presents BioRAGent, a system that improves biomedical Q&A by combining few-shot LLM query expansion with PubMed-based document retrieval.
- It employs a two-stage approach for query expansion and snippet extraction to ensure evidence-based, transparent answers with inline citations.
- Evaluation in the BioASQ 2024 challenge shows competitive performance, demonstrating its potential for reliable, domain-specific biomedical search.
Overview of BioRAGent: Retrieval-Augmented Generation for Biomedical Q&A
The paper presents BioRAGent, an advanced retrieval-augmented generation (RAG) system specifically designed for professional biomedical question answering (Q&A). This work is situated in the context of emerging capabilities offered by LLMs to substantially improve the field of information retrieval, especially under the constraints of complex, domain-specific requirements such as those in the biomedical sector. The system was developed as a response to the challenges faced in the application of LLMs, particularly their propensity to hallucinate, thus compromising reliability when directly applied to professional search environments.
System Description
BioRAGent's architecture involves specialized integration of LLMs with retrieval mechanisms to ensure more accurate and evidence-based results. It utilizes a few-shot learning approach with LLMs to handle tasks such as query expansion, document retrieval, and snippet extraction, attributing improvements in output veracity to the underlying structured data from reliable sources like PubMed. The system comprises several core components:
- Query Expansion: Employing few-shot learning enables the generation of expanded queries that incorporate synonyms and related terms, enhancing retrieval effectiveness. Users have the flexibility to review and edit these queries post-expansion.
- Document Retrieval and Snippet Extraction: The system retrieves top-ranked documents from a PubMed index using an Elasticsearch-supported query process. Subsequent snippet extraction is performed using LLMs to ensure relevance to the original question.
- Answer Generation: The system adapts two distinct answer formats: concise paragraph responses and detailed answers with inline citations to retrieved sources, promoting transparency and traceability.
- User Interface: A web-based interface allows users to input queries, view expanded results, and access answers alongside source snippets, facilitating a comprehensive understanding of the response context.
Evaluation
BioRAGent was evaluated through its participation in the BioASQ 2024 challenge, where it demonstrated competitive results in the biomedical Q&A task. It secured multiple placements across varied task formats, highlighting its efficacy in snippet extraction and question answering.
Implications and Future Directions
BioRAGent contributes to the landscape of professional search by integrating reliable source-grounding mechanisms within complex biomedical information retrieval. Its transparent query expansion process distinguishes it from dense vector search techniques, offering a controllable and interpretable search experience. This aligns particularly well with professional practices that demand high verifiability and transparency.
The paper suggests several future enhancements for BioRAGent, including live prompt editing capabilities, augmentation of hallucination detection methods, and the exploration of alternative LLMs. These endeavors will potentially refine and advance the system's applicability in real-world biomedical search scenarios. Indications for future work also emphasize continuous improvements in few-shot learning methodologies, which could offer more robust and adaptable query generation mechanisms. This work promises to seed further research in the domain of retrieval-augmented generation systems, encouraging exploration into similar applications across other specialized fields.