Year: 2026
Type: conference-paper
Abstract: Multi-turn retrieval-augmented generation (RAG) is challenging due to evolving user intent, conversational noise, and strict context limits.We propose a training-free, hybrid retrieval pipeline for SemEval-2026 Task 8 that combines dense and sparse retrieval with controlled query rewriting and cross-encoder reranking.On the official test set of Task A, our system achieves 0.5453 nDCG@5, ranking third among 38 teams and outperforming the strongest baseline (0.4795).For Task C, we reuse the documents retrieved for Task A and apply a lightweight generation pipeline guided by the official prompt, achieving 0.5312 (the harmonic mean of relevance and faithfulness) and ranking 15th out of 29 teams.All retrieval components are open-source, while rewriting and generation rely on LLM APIs.Code and scripts are available on GitHub 1 .
Authors: Sifei Meng, Dmitry Ilvovsky
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166879014