Papers for

medical information specialists

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Lit3R system retrieves and reads science papers to answer questions

Lit3R: Retrieve-Relate-Read for Evidence-Grounded Question Answering over Scientific Literature

Abstract: We describe tus-nlp's Lit3R (Retrieve-Relate-Read) system for LitTraceQA, a shared task for literature-grounded question answering that requires systems to retrieve relevant papers, identify supporting evidence, and generate answers. Lit3R combines off-the-shelf retrieval, reranking, and large language model (LLM) components without task-specific training. The retriever iteratively combines BM25-based sparse and dense retrieval, cross-encoder reranking, and LLM-based verification, and complements retrieval based on the question with paper-to-paper expansion. The reader first identifies supporting evidence within individual papers and then synthesizes evidence across papers to produce the final answer and evidence trace. On the official test set, our system ranked 4th on the leaderboard. Our code is available at https://github.com/tus-ist-nlp/littraceqa.

Tue 15 SeptComputation and Language

The gist

Answering questions with evidence from many scientific papers is hard because you first need to find the right papers, then figure out the important parts, and finally give a clear answer. The authors created Lit3R, a system that looks up papers using different search methods, checks if they really match the question, and then picks out evidence from those papers. It combines information from multiple papers to give a final answer along with the evidence it found. This system was tested in a competition and ranked fourth overall.

Open → 2609.16912v1