用稀疏、语义与大模型重排，提升注意力缺陷症状句子相关性排序。
DS@GT-ARC at eRisk 2026 Task 3: Sparse, Semantic, and LLM Reranking for ADHD Symptom Sentences
- 分阶段检索：先用BM25粗筛，再通过语义和大模型精排。
- 大模型重排在官方评测中得分最高，优于查询扩展方案。
- 适用于无标注数据的零样本场景，适合精神健康文本排序任务。
本文介绍我们对eRisk 2026任务3（成人注意缺陷多动障碍症状句子排序）的参赛方案。该任务要求根据成人ADHD自评量表（ASRS-v1.1）中的18个症状，对候选Reddit句子进行相关性排序。由于本年度首次发布任务且未提供标注训练数据，我们采用零样本实验、人工验证及无监督或弱指导检索流程。系统结合了稀疏BM25检索、面向自述症状的证据感知重排、基于嵌入的重排、查询原型扩展和大语言模型（LLM）重排。所有提交系统均采用分阶段检索设计：先由BM25大规模召回候选句，再由语义或LLM重排器优化最终排序。在各项提交中，大语言模型重排器取得最佳官方成绩，其次为查询原型扩展方案。人工分析前10名结果与专家评分趋势一致，表明分阶段重排是未来发展的有前景方向。
原文摘要 · Abstract (English)
This paper describes our submissions to eRisk 2026 Task 3, ADHD Symptom Sentence Ranking. The task requires systems to rank candidate Reddit sentences according to their relevance to each of the 18 symptoms in the Adult ADHD Self-Report Scale (ASRS-v1.1). Because no annotated training data were released for this first edition of the task, we relied on zero-shot experimentation, manual validation, and unsupervised or weakly guided retrieval pipelines. Our systems combine sparse BM25 retrieval, evidence-aware rescoring for self-referential symptom reports, embedding-based reranking, query-prototype expansion, and LLM-based reranking. All submitted systems follow a staged retrieval design in which BM25 retrieves candidates at scale and semantic or LLM rerankers refine the final rankings. Among our submissions, the LLM reranker achieved the strongest official scores, followed by the prototype query-expansion run. Our manual top-10 analysis aligned with the official expert scoring trend, suggesting that staged reranking is a promising direction for further development.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。