@inproceedings{wigger-etal-2026-ragtum,
title = "{RAGTUM} at {S}em{E}val-2026 Task 8: Contextual Query Rewriting and Dense Retrieval for Multi-Turn {RAG}",
author = "Wigger, Finn and
Podolsky, Maximilian and
Wilmink, Merle and
Peng, Zelong",
editor = "Kochmar, Ekaterina and
Ghosh, Debanjan and
North, Kai and
Komachi, Mamoru",
booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.semeval-1.227/",
pages = "1784--1790",
ISBN = "979-8-89176-414-9",
abstract = "This paper describes the system developed by a team for the TUM practical course Human-Centered Computing: applications in natural language processing, network science, machine learning, and AI for the SemEval MTRAG. Our approach addresses the challenges of multi-turn retrieval-augmented generation (RAG) by combining context-aware query rewriting with a dense retrieval strategy. We employ a pipeline that cleanses noisy corpora and utilizes dense OpenAI embeddings via Milvus for robust retrieval, and leverages Gemini 2.5 flash family of models for standalone query generation and final response synthesis. Our system demonstrates the effectiveness of integrating high-precision retrieval with fact-based generation across diverse domains."
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<abstract>This paper describes the system developed by a team for the TUM practical course Human-Centered Computing: applications in natural language processing, network science, machine learning, and AI for the SemEval MTRAG. Our approach addresses the challenges of multi-turn retrieval-augmented generation (RAG) by combining context-aware query rewriting with a dense retrieval strategy. We employ a pipeline that cleanses noisy corpora and utilizes dense OpenAI embeddings via Milvus for robust retrieval, and leverages Gemini 2.5 flash family of models for standalone query generation and final response synthesis. Our system demonstrates the effectiveness of integrating high-precision retrieval with fact-based generation across diverse domains.</abstract>
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%0 Conference Proceedings
%T RAGTUM at SemEval-2026 Task 8: Contextual Query Rewriting and Dense Retrieval for Multi-Turn RAG
%A Wigger, Finn
%A Podolsky, Maximilian
%A Wilmink, Merle
%A Peng, Zelong
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F wigger-etal-2026-ragtum
%X This paper describes the system developed by a team for the TUM practical course Human-Centered Computing: applications in natural language processing, network science, machine learning, and AI for the SemEval MTRAG. Our approach addresses the challenges of multi-turn retrieval-augmented generation (RAG) by combining context-aware query rewriting with a dense retrieval strategy. We employ a pipeline that cleanses noisy corpora and utilizes dense OpenAI embeddings via Milvus for robust retrieval, and leverages Gemini 2.5 flash family of models for standalone query generation and final response synthesis. Our system demonstrates the effectiveness of integrating high-precision retrieval with fact-based generation across diverse domains.
%U https://aclanthology.org/2026.semeval-1.227/
%P 1784-1790
Markdown (Informal)
[RAGTUM at SemEval-2026 Task 8: Contextual Query Rewriting and Dense Retrieval for Multi-Turn RAG](https://aclanthology.org/2026.semeval-1.227/) (Wigger et al., SemEval 2026)
ACL