@inproceedings{he-etal-2026-radam,
title = "{RADAM}: A Retrieval-Augmented Detection and Mitigation Framework for Faithfulness Hallucinations",
author = "He, Bin and
Li, Guanghua and
Liu, Yu and
Zhou, Mingyang and
Mao, Rui and
Chao, Naipeng and
Liao, Hao",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1573/",
pages = "33507--33527",
ISBN = "979-8-89176-462-0",
abstract = "Retrieval-augmented generation (RAG) empowers large language models (LLMs) with external knowledge but remains vulnerable to faithfulness hallucinations{---}answers that contradict retrieved evidence. We introduce RADAM, a lightweight post-processing framework that mitigates this issue via fine-grained hallucination detection and guided revision. RADAM leverages a single diagnostic critic, optimized via preference tuning on taxonomy-guided data, to generate structured critiques encompassing both precise error spans and explicit revision suggestions, which guide the base model toward more faithful outputs. Experiments on real-world datasets demonstrate that RADAM consistently achieves the lowest average hallucination rate across the evaluated methods, while largely preserving response quality and achieving out-of-domain performance comparable to enterprise-grade LLMs."
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<abstract>Retrieval-augmented generation (RAG) empowers large language models (LLMs) with external knowledge but remains vulnerable to faithfulness hallucinations—answers that contradict retrieved evidence. We introduce RADAM, a lightweight post-processing framework that mitigates this issue via fine-grained hallucination detection and guided revision. RADAM leverages a single diagnostic critic, optimized via preference tuning on taxonomy-guided data, to generate structured critiques encompassing both precise error spans and explicit revision suggestions, which guide the base model toward more faithful outputs. Experiments on real-world datasets demonstrate that RADAM consistently achieves the lowest average hallucination rate across the evaluated methods, while largely preserving response quality and achieving out-of-domain performance comparable to enterprise-grade LLMs.</abstract>
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%0 Conference Proceedings
%T RADAM: A Retrieval-Augmented Detection and Mitigation Framework for Faithfulness Hallucinations
%A He, Bin
%A Li, Guanghua
%A Liu, Yu
%A Zhou, Mingyang
%A Mao, Rui
%A Chao, Naipeng
%A Liao, Hao
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F he-etal-2026-radam
%X Retrieval-augmented generation (RAG) empowers large language models (LLMs) with external knowledge but remains vulnerable to faithfulness hallucinations—answers that contradict retrieved evidence. We introduce RADAM, a lightweight post-processing framework that mitigates this issue via fine-grained hallucination detection and guided revision. RADAM leverages a single diagnostic critic, optimized via preference tuning on taxonomy-guided data, to generate structured critiques encompassing both precise error spans and explicit revision suggestions, which guide the base model toward more faithful outputs. Experiments on real-world datasets demonstrate that RADAM consistently achieves the lowest average hallucination rate across the evaluated methods, while largely preserving response quality and achieving out-of-domain performance comparable to enterprise-grade LLMs.
%U https://aclanthology.org/2026.findings-emnlp.1573/
%P 33507-33527
Markdown (Informal)
[RADAM: A Retrieval-Augmented Detection and Mitigation Framework for Faithfulness Hallucinations](https://aclanthology.org/2026.findings-emnlp.1573/) (He et al., Findings 2026)
ACL