Problem
Research questions and friction points this paper is trying to address.
generative retrieval
scientific literature exploration
facet-aware
GRAFT: Graph-Distilled Generative Retrieval for Facet-Aware Scientific Literature Exploration
🤖 AI Summary
本文提出GRAFT方法，通过构建和提炼包含问题、方法、结果和贡献四个方面的论文图谱，实现生成式检索以支持科学文献的探索性搜索。
📝 Abstract
Scientific papers may relate by problem, method, result, or contribution, but document-level retrievers collapse these into a single similarity score without saying why they are related. Citation- and similarity-based retrieval alone also confines search to the neighbourhood of what is already known, whereas generative retrieval generates document identifiers directly, enabling the exploratory retrieval that scientific discovery depends on. We connect papers in a graph whose edges are typed by these four facets, derived from facet items and citation signals, and distil it into a generative retriever whose identifiers are the papers' own facet text. Two graph properties do not survive naive distillation. First, because every training pair is an edge, naive enumeration indexes just 84% of the corpus. Coverage-aware distillation makes every paper learnable through a reverse-neighbour fallback, a minimum-coverage threshold, and edge-importance weighting. Second, constrained decoding guarantees that every generated identifier is a valid paper, but not that the graph connects it to the query. Graph-weighted reciprocal rank fusion scales each candidate's rank term by its query-candidate edge weight, dropping unsupported ones. On LitWeave, our constructed corpus of 11,359 NLP papers, Graft recovers 91% of its graph teacher's Recall@20 with no nearest-neighbour index or encoder at inference, and outperforms the graph teacher on query papers outside the corpus. It reproduces the graph's own facet labels at 0.922 precision, so every returned paper arrives labelled with the facet that surfaced it rather than an opaque score.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Graph-Distilled
Generative Retrieval
Facet-Aware
Coverage-aware Distillation
Graph-Weighted Reciprocal Rank Fusion
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