UTRAG at SemEval-2026 Task 8: History-Aware Query Rewriting and LoRA-Finetuned Generation for Multi-Turn RAG
Year: 2026
Type: conference-paper
Abstract: This paper describes our system for SemEval-2026 Task 8: Evaluating Multi-Turn RAG Conversations (MTRAGEval), which evaluates retrieval-augmented generation (RAG) in multiturn, context-dependent settings.We improve retrieval with history-aware query rewriting and enhance generation faithfulness with a LoRAadapted model, integrating both into an end-toend pipeline.Our approach achieves competitive performance across all subtasks, with nDCG@5 of 0.4855 in Subtask A, a harmonic mean score of 0.6554 in Subtask B, and 0.5159 in Subtask C, outperforming strong baselines in Subtasks A and B while remaining competitive in Subtask C.Our analysis shows that increasing dialogue length introduces cumulative errors in history selection and query formulation, leading to incomplete or drifting retrieval results and increasing the risk of hallucination.
Authors: Ke Zhou, Yi-Shan Lin
Institution: University of Tübingen
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166818798