Problem
Research questions and friction points this paper is trying to address.
Evidence-Grounded Question Answering
Scientific Literature
Retrieve-Relate-Read
Lit3R: Retrieve-Relate-Read for Evidence-Grounded Question Answering over Scientific Literature
🤖 AI Summary
为解决文献支撑的问题回答，Lit3R系统采用检索-关联-阅读方法，结合现成的检索、重排序和大型语言模型组件，无需特定任务训练。
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Retrieve-Relate-Read
BM25-based retrieval
dense retrieval
cross-encoder reranking
LLM-based verification
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