Large language models (LLMs) demonstrate remarkable capabilities but face challenges from hallucinations, which typically arise from insufficient knowledge or context. While instructing LLMs to acknowledge knowledge limitations by responding with "I don't know" appears promising, we find that models consistently struggle with admitting knowledge gaps. This challenge may originate from current instruction datasets that emphasise answer generation over knowledge boundary awareness. To address this limitation, we introduce Uncertainty-and-Sensitivity-Aware Tuning (US-Tuning), a novel two-stage approach for contextual question answering (QA). The first stage enhances LLMs' ability to recognise their knowledge boundaries, while the second stage reinforces instruction adherence through carefully designed causal prompts. Our experimental results demonstrate that US-Tuning not only significantly reduces incorrect answers in contextual QA but also improves models' faithfulness to their parametric knowledge, mitigating hallucinations in general QA tasks. Our fine-tuned Llama2-7B model achieves up to a 34.7% improvement in handling out-of-knowledge questions and outperforms GPT-4 by 4.2% in overall performance.
Utilize the Flow before Stepping into the Same River Twice: Certainty Represented Knowledge Flow for Refusal-Aware Instruction Tuning
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Peking University（北京大学）；Southwest Jiaotong University（西南交通大学）；Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences（中国科学院大学杭州高等研究院）
MLAN: Language-Based Instruction Tuning Preserves and Transfers Knowledge in Multimodal Language Models
Washington University in St. Louis（华盛顿大学圣路易斯分校）；The University of British Columbia（不列颠哥伦比亚大学）；Google Research（谷歌研究）；Virginia Tech（弗吉尼亚理工大学）；University of California, Davis（加州大学戴维斯分校）；Sony AI（索尼人工智能）；University of California, Berkeley（加州大学伯克利分校）
Multilingual Pretraining and Instruction Tuning Improve Cross-Lingual Knowledge Alignment, But Only Shallowly
National Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）；Department of Linguistics and Translation, City University of Hong Kong（香港城市大学语言学及翻译学系）
Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning
Department of Gastroenterology, Renmin Hospital, Wuhan University（消化内科，仁医医院，武汉大学）；School of Computer Science, Wuhan University（计算机学院，武汉大学）；School of Computer Science, Faculty of Engineering, The University of Sydney（计算机学院，工程学院，悉尼大学）
Quantifying LLM Biases Across Instruction Boundary in Mixed Question Forms
Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；University of Pennsylvania（宾夕法尼亚大学）；Huazhong University of Science and Technology（华中科技大学）；Hong Kong Polytechnic University（香港理工大学）；Nanjing University of Posts and Telecommunications（南京邮电大学）
Synthesizing Instruction-Tuning Datasets with Contrastive Decoding
Department of Computer Science, School of Computing, Institute of Science Tokyo（东京科学大学计算机科学系）；National Institute of Advanced Industrial Science and Technology（国家先进工业科学与技术研究院）；Research and Development Center for Large Language Models, NII（大型语言模型研究与开发中心，日本信息产业技术综合研究所）
GRAIT: Gradient-Driven Refusal-Aware Instruction Tuning for Effective Hallucination Mitigation
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Peking University（北京大学）；Southwest Jiaotong University（西南交通大学）；Shanghai Jiaotong University（上海交通大学）；Chinese University of Hong Kong（香港中文大学）
Multi-Agent Collaboration for Multilingual Code Instruction Tuning
Alibaba Group（阿里巴巴集团）；Shanghai Jiao Tong University（上海交通大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）
Learning to Edit Knowledge via Instruction-based Chain-of-Thought Prompting
Beijing University of Posts and Telecommunications（北京邮电大学）；Peking University（北京大学）；Huawei Technologies Ltd.（华为技术有限公司）；Chongqing University of Posts and Telecommunications（重庆邮电大学）
Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models
Microsoft（微软）；Singapore University of Technology and Design（新加坡科技设计大学）；Peking University（北京大学）；Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Chinese University of Hong Kong（香港中文大学）；Tsinghua University（清华大学）
Enhancing LLM Tool Use with High-quality Instruction Data from Knowledge Graph
Ant Group, China（蚂蚁集团）；School of Computer Science and Technology, Xi’an Jiaotong University, China（西安交通大学计算机科学与技术学院）；School of Distance Education, Xi’an Jiaotong University, China（西安交通大学继续教育学院）
InstructProtein: Aligning Human and Protein Language via Knowledge Instruction
Zhejiang University（浙江大学）；ZJU-Hangzhou Global Scientific and Technological Innovation Center（浙江大学杭州国际科创中心）；AZFT Joint Lab for Knowledge Engine（AZFT知识引擎联合实验室）
Finetuning Generative Large Language Models with Discrimination Instructions for Knowledge Graph Completion
State Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）；National Institute of Healthcare Data Science, Nanjing University（南京大学国家卫生健康数据科学研究院）
ProSwitch: Knowledge-Guided Instruction Tuning to Switch Between Professional and Non-Professional Responses
Zhejiang University of Science and Technology（浙江科技大学）；Fudan University（复旦大学）；Zhejiang University（浙江大学）；The Chinese University of Hong Kong（香港中文大学）；Tsinghua University（清华大学）
X-Instruction: Aligning Language Model in Low-resource Languages with Self-curated Cross-lingual Instructions
State Key Laboratory of Multimodal Artificial Intelligence Systems（多模态人工智能系统国家重点实验室）；Institute of Automation, CAS（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Bridging Auxiliary Constraints to Resolve Instruction Following in Large Reasoning Models
The Chinese University of Hong Kong（香港中文大学）；University of International Relations（国际关系大学）；Tencent Jarvis Lab（腾讯Jarvis实验室）；Westlake University（西湖大学）；King’s College London（伦敦国王学院）
