@inproceedings{t-nguyen-etal-2026-5ting,
title = "5ting at {S}em{E}val-2026 Task 8: Strong End-to-End Multi-Turn {RAG} via {LLM}-Based Reranking and Faithfulness Control",
author = "T-Nguyen, Thien-Qua and
Hoang, Chi and
Tran, Nguyen and
Le, Tri and
Truong, Khanh and
Nguyen, Chinh",
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-1.254/",
pages = "2026--2033",
ISBN = "979-8-89176-414-9",
abstract = "This paper presents a modular multi-turn Retrieval-Augmented Generation (RAG) system designed to mitigate hallucination, context drift, and underspecification. The pipeline combines dual-query merged retrieval and LLM-based reranking to deliver high-precision evidence, improving nDCG@5 by 17.7{\%}. To strictly control hallucination during generation, we introduce a role-separated prompting strategy. - This approach explicitly isolates the conversation history (used solely for intent and coreference resolution) from the retrieved passages (enforced as the exclusive source of factual grounding). - By preventing the language model from misinterpreting prior dialogue turns as factual evidence, the system ranked 3/29 in the SemEval-2026 Task 8 end-to-end evaluation. - Notably, our faithfulness-oriented design achieved a high ROUGE-L F1 score of 0.7692, outperforming larger baselines and demonstrating that explicit grounding constraints are highly effective at ensuring lexical faithfulness and reducing hallucinations."
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<abstract>This paper presents a modular multi-turn Retrieval-Augmented Generation (RAG) system designed to mitigate hallucination, context drift, and underspecification. The pipeline combines dual-query merged retrieval and LLM-based reranking to deliver high-precision evidence, improving nDCG@5 by 17.7%. To strictly control hallucination during generation, we introduce a role-separated prompting strategy. - This approach explicitly isolates the conversation history (used solely for intent and coreference resolution) from the retrieved passages (enforced as the exclusive source of factual grounding). - By preventing the language model from misinterpreting prior dialogue turns as factual evidence, the system ranked 3/29 in the SemEval-2026 Task 8 end-to-end evaluation. - Notably, our faithfulness-oriented design achieved a high ROUGE-L F1 score of 0.7692, outperforming larger baselines and demonstrating that explicit grounding constraints are highly effective at ensuring lexical faithfulness and reducing hallucinations.</abstract>
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%0 Conference Proceedings
%T 5ting at SemEval-2026 Task 8: Strong End-to-End Multi-Turn RAG via LLM-Based Reranking and Faithfulness Control
%A T-Nguyen, Thien-Qua
%A Hoang, Chi
%A Tran, Nguyen
%A Le, Tri
%A Truong, Khanh
%A Nguyen, Chinh
%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 t-nguyen-etal-2026-5ting
%X This paper presents a modular multi-turn Retrieval-Augmented Generation (RAG) system designed to mitigate hallucination, context drift, and underspecification. The pipeline combines dual-query merged retrieval and LLM-based reranking to deliver high-precision evidence, improving nDCG@5 by 17.7%. To strictly control hallucination during generation, we introduce a role-separated prompting strategy. - This approach explicitly isolates the conversation history (used solely for intent and coreference resolution) from the retrieved passages (enforced as the exclusive source of factual grounding). - By preventing the language model from misinterpreting prior dialogue turns as factual evidence, the system ranked 3/29 in the SemEval-2026 Task 8 end-to-end evaluation. - Notably, our faithfulness-oriented design achieved a high ROUGE-L F1 score of 0.7692, outperforming larger baselines and demonstrating that explicit grounding constraints are highly effective at ensuring lexical faithfulness and reducing hallucinations.
%U https://aclanthology.org/2026.semeval-1.254/
%P 2026-2033
Markdown (Informal)
[5ting at SemEval-2026 Task 8: Strong End-to-End Multi-Turn RAG via LLM-Based Reranking and Faithfulness Control](https://aclanthology.org/2026.semeval-1.254/) (T-Nguyen et al., SemEval 2026)
ACL