提出统一框架提升多轮RAG对话效果，三任务表现领先。
AILS-NTUA at SemEval-2026 Task 8: Evaluating Multi-Turn RAG Conversations
- 用五种查询改写+方差感知融合，提升检索多样性。
- 任务A nDCG@5达0.5776，比最强基线高20.5%。
- 适合研究多轮对话生成与检索增强系统的人参考。
我们介绍AILS-NTUA系统参与SemEval-2026 Task 8（MTRAGEval），涵盖多轮检索增强生成的三个子任务：段落检索（A）、基于引用的回答生成（B）和端到端RAG（C）。系统采用统一架构，基于两个原则：(i) 查询多样性优于检索器多样性，即对单一语料对齐的稀疏检索器发出五种互补的LLM查询改写，并通过方差感知嵌套倒数排名融合；(ii) 多阶段生成流程，将受控生成分解为证据片段抽取、双候选草稿和校准式多裁判选择。系统在任务A中排名第一（nDCG@5: 0.5776，较最强基线提升20.5%），任务B中排名第二（HM: 0.7698）。实证分析表明，查询多样性在良好对齐的检索器上优于异构检索器集成，且答案可回答性校准——而非检索覆盖率——是端到端性能的主要瓶颈。
原文摘要 · Abstract (English)
We present the AILS-NTUA system for SemEval-2026 Task 8 (MTRAGEval), addressing all three subtasks of multi-turn retrieval-augmented generation: passage retrieval (A), reference-grounded response generation (B), and end-to-end RAG (C). Our unified architecture is built on two principles: (i) a query-diversity-over-retriever-diversity strategy, where five complementary LLM-based query reformulations are issued to a single corpus-aligned sparse retriever and fused via variance-aware nested Reciprocal Rank Fusion; and (ii) a multistage generation pipeline that decomposes grounded generation into evidence span extraction, dual-candidate drafting, and calibrated multi-judge selection. Our system ranks 1st in Task A (nDCG@5: 0.5776, +20.5% over the strongest baseline) and 2nd in Task B (HM: 0.7698). Empirical analysis shows that query diversity over a well-aligned retriever outperforms heterogeneous retriever ensembling, and that answerability calibration-rather than retrieval coverage-is the primary bottleneck in end-to-end performance.
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