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-5.2 and o3. Also, FaithLens can produce high-quality explanations, delivering a distinctive balance of trustworthiness, efficiency, and effectiveness.
CogniBench: A Legal-inspired Framework and Dataset for Assessing Cognitive Faithfulness of Large Language Models
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Hunyuan AI Digital Human, Tencent（文心一言数字人，腾讯）；Beijing University of Posts and Telecommunications（北京邮电大学）；Shanghai AI Laboratory（上海人工智能实验室）
Don't Believe Everything You Read: Enhancing Summarization Interpretability through Automatic Identification of Hallucinations in Large Language Models
Copy-Paste to Mitigate Large Language Model Hallucinations
Department of Computer Science, Tianjin University of Technology（天津理工大学计算机学院）；National Institute of Health Data Science, Peking University（北京大学国家健康数据科学研究院）；Tencent Jarvis Lab（腾讯Jarvis实验室）
AraHalluEval: A Fine-grained Hallucination Evaluation Framework for Arabic LLMs
Information and Computer Science Department, King Fahd University of Petroleum and Minerals（信息与计算机科学系，国王法赫德石油和矿物大学）；SDAIA-KFUPM Joint Research Center for Artificial Intelligence（SDAIA-KFUPM人工智能联合研究中心）
Stop Rewarding Hallucinated Steps: Faithfulness-Aware Step-Level Reinforcement Learning for Small Reasoning Models
Institute of Computing and Intelligence, Harbin Institute of Technology, Shenzhen, China（哈尔滨工业大学深圳研究院）；Institute of Artificial Intelligence (TeleAI), China Telecom Corp Ltd（中国电信人工智能研究院）；College of Integrated Circuits, Zhejiang University, Hangzhou, Zhejiang, China（浙江大学集成电路学院）；Zhongguancun Academy, Beijing, China（中关村学院）
SSFO: Self-Supervised Faithfulness Optimization for Retrieval-Augmented Generation
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Chinese Academy of Sciences（中国科学院）；Shanghai AI Lab（上海人工智能实验室）；Tencent Hunyuan（腾讯文英）
5ting at SemEval-2026 Task 8: Strong End-to-End Multi-Turn RAG via LLM-Based Reranking and Faithfulness Control
University of Information Technology, Ho Chi Minh City, Vietnam（信息技术大学，胡志明市，越南）；Vietnam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam（越南胡志明市国家大学，胡志明市，越南）
Is Factuality Enhancement a Free Lunch For LLMs? Better Factuality Can Lead to Worse Context-Faithfulness
Institute of Computing Technology, CAS（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；University of California, Merced（加州大学默塞德分校）；University of Science and Technology of China（中国科学技术大学）
Hallucination Detection-Guided Preference Optimization for Clinical Summarization
Manning College of Information and Computer Sciences（Manning信息与计算机科学学院）；Ensemble HP；Columbia University（哥伦比亚大学）；University of Massachusetts Amherst（马萨诸塞大学阿姆赫斯特分校）
