HalluGuard: Evidence-Grounded Small Reasoning Models to Mitigate
  Hallucinations in Retrieval-Augmented Generation
Large Language Models (LLMs) excel in many NLP tasks but remain prone to
hallucinations, limiting trust in real-world applications. We present
HalluGuard, a 4B-parameter Small Reasoning Model (SRM) for mitigating
hallucinations in Retrieval-Augmented Generation (RAG). HalluGuard classifies
document-claim pairs as grounded or hallucinated and produces evidence-grounded
justifications for transparency. Our approach combines (i) a domain-agnostic
synthetic dataset derived from FineWeb and refined through multi-stage curation
and data reformation, (ii) synthetic grounded and hallucinated claims, and
(iii) preference-based fine-tuning with Odds Ratio Preference Optimization to
distill large-model reasoning into a smaller backbone. On the RAGTruth subset
of the LLM-AggreFact benchmark, HalluGuard achieves 84.0% balanced accuracy
(BAcc), rivaling specialized models, MiniCheck (7B; 84.0%) and Granite Guardian
3.3 (8B; 82.2%) while using roughly half their parameters. Over the full
benchmark it reaches 75.7% BAcc, matching larger general-purpose LLMs such as
GPT-4o (75.9%). We will release HalluGuard and datasets under Apache 2.0 upon
acceptance.
68e0c65d73e20ab577841cb0
Loris Bergeron
claimed_verified
2025-10-05T12:45:43.696Z
65956fe943930939ed44ffa6
https://cdn-avatars.huggingface.co/v1/production/uploads/65956fe943930939ed44ffa6/omZnoR5psFPrKGBu099W0.jpeg
Loris Bergeron
user
lrsbrgrn
68e0c65d73e20ab577841cb1
Ioana Buhnila
68e0c65d73e20ab577841cb2
Jérôme François
68e0c65d73e20ab577841cb3
Radu State
HalluGuard: Evidence-Grounded Small Reasoning Models to Mitigate
  Hallucinations in Retrieval-Augmented Generation
Large Language Models (LLMs) excel in many NLP tasks but remain prone to
hallucinations, limiting trust in real-world applications. We present
HalluGuard, a 4B-parameter Small Reasoning Model (SRM) for mitigating
hallucinations in Retrieval-Augmented Generation (RAG). HalluGuard classifies
document-claim pairs as grounded or hallucinated and produces evidence-grounded
justifications for transparency. Our approach combines (i) a domain-agnostic
synthetic dataset derived from FineWeb and refined through multi-stage curation
and data reformation, (ii) synthetic grounded and hallucinated claims, and
(iii) preference-based fine-tuning with Odds Ratio Preference Optimization to
distill large-model reasoning into a smaller backbone. On the RAGTruth subset
of the LLM-AggreFact benchmark, HalluGuard achieves 84.0% balanced accuracy
(BAcc), rivaling specialized models, MiniCheck (7B; 84.0%) and Granite Guardian
3.3 (8B; 82.2%) while using roughly half their parameters. Over the full
benchmark it reaches 75.7% BAcc, matching larger general-purpose LLMs such as
GPT-4o (75.9%). We will release HalluGuard and datasets under Apache 2.0 upon
acceptance.
HalluGuard, a 4B-parameter Small Reasoning Model, effectively mitigates hallucinations in Retrieval-Augmented Generation by classifying document-claim pairs and providing evidence-grounded justifications, achieving high balanced accuracy on the LLM-AggreFact benchmark.
Large Language Models HalluGuard Small Reasoning Model Retrieval-Augmented Generation document-claim pairs grounded hallucinated evidence-grounded justifications FineWeb multi-stage curation data reformation preference-based fine-tuning Odds Ratio Preference Optimization RAGTruth LLM-AggreFact benchmark MiniCheck Granite Guardian GPT-4o balanced accuracy