FaithLens: Detecting and Explaining Faithfulness Hallucination
Recognizing whether outputs from large language models (LLMs) contain faithfulness hallucination is crucial for real-world applications, e.g., retrieval-augmented generation and summarization. In this paper, we introduce FaithLens, a cost-efficient and effective faithfulness hallucination detection model that can jointly provide binary predictions and corresponding explanations to improve trustworthiness. To achieve this, we first synthesize training data with explanations via advanced LLMs and apply a well-defined data filtering strategy to ensure label correctness, explanation quality, and data diversity. Subsequently, we fine-tune the model on these well-curated training data as a cold start and further optimize it with rule-based reinforcement learning, using rewards for both prediction correctness and explanation quality. Results on 12 diverse tasks show that the 8B-parameter FaithLens outperforms advanced models such as GPT-4.1 and o3. Also, FaithLens can produce high-quality explanations, delivering a distinctive balance of trustworthiness, efficiency, and effectiveness.
694b7923746a34b55dd53df4
Shuzheng Si
694b7923746a34b55dd53df5
Qingyi Wang
694b7923746a34b55dd53df6
Haozhe Zhao
694b7923746a34b55dd53df7
Yuzhuo Bai
claimed_verified
2026-01-22T17:12:38.675Z
6426d94ba7723d62b53c4b5a
/avatars/63db93f24e1e32e310f958f03e91d2c7.svg
Yuzhuo Bai
Phosphor-Bai
user
Phosphor-Bai
694b7923746a34b55dd53df8
Guanqiao Chen
694b7923746a34b55dd53df9
Kangyang Luo
694b7923746a34b55dd53dfa
Gang Chen
694b7923746a34b55dd53dfb
Fanchao Qi
694b7923746a34b55dd53dfc
Minjia Zhang
694b7923746a34b55dd53dfd
Baobao Chang
694b7923746a34b55dd53dfe
Maosong Sun
FaithLens: Detecting and Explaining Faithfulness Hallucination
Recognizing whether outputs from large language models (LLMs) contain faithfulness hallucination is crucial for real-world applications, e.g., retrieval-augmented generation and summarization. In this paper, we introduce FaithLens, a cost-efficient and effective faithfulness hallucination detection model that can jointly provide binary predictions and corresponding explanations to improve trustworthiness. To achieve this, we first synthesize training data with explanations via advanced LLMs and apply a well-defined data filtering strategy to ensure label correctness, explanation quality, and data diversity. Subsequently, we fine-tune the model on these well-curated training data as a cold start and further optimize it with rule-based reinforcement learning, using rewards for both prediction correctness and explanation quality. Results on 12 diverse tasks show that the 8B-parameter FaithLens outperforms advanced models such as GPT-4.1 and o3. Also, FaithLens can produce high-quality explanations, delivering a distinctive balance of trustworthiness, efficiency, and effectiveness.
FaithLens, a cost-efficient faithfulness hallucination detection model using advanced LLMs for training data synthesis and rule-based reinforcement learning, outperforms models like GPT-4.1 and o3 on 12 tasks with high-quality explanations.
faithfulness hallucination FaithLens large language models (LLMs) retrieval-augmented generation summarization binary predictions explanations data filtering strategy cold start rule-based reinforcement learning prediction correctness explanation quality data diversity trustworthiness efficiency effectiveness