Year: 2026
Type: conference-paper
Abstract: We present the results and findings from Se-mEval Task 8: MTRAGEval.MTRAGEval measures three Retrieval Augmented Generation (RAG) subtasks: A. Retrieval, B. Generate, and C. Retrieve+Generate (full RAG) on multiturn conversations.The task is evaluated using MTRAG-UN, a new benchmark for Multi-Turn RAG focusing on Unanswerable, Underspecified, Non-Standalone, and Unclear Questions.The MTRAGEval task attracted strong participation with 107 registered teams and 92 submissions across all subtasks, and yielded several interesting findings on effective retrieval and query rewriting techniques, the use of ensemble models, and the compounding costs of retrieval errors on downstream generation quality.
Institution: IBM (United States)
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166872946