Large language models (LLMs) often struggle with knowledge intensive NLP tasks, such as answering "Who won the latest World Cup?" because the knowledge they learn during training may be insufficient or outdated. Conditioning generation on retrieved documents -- a technique known as retrieval augmented generation (RAG) -- mitigates these shortcomings by allowing the model to leverage in-context information. Practitioners can improve LLM RAG performance by fine-tuning on retrieval-augmented instructions, but must beware that this can cause undesirable model behaviors like hallucinations. We attribute this degradation to the fact that the training data is likely to be out-of-distribution for the model and may suffer from quality issues, such as misalignment between retrievals and target responses (since retrievals are frequently added post-hoc). We propose a recipe for training RAG-enabled LLMs using self-generated demonstrations, thereby avoiding training on out-of-distribution text and integrating retrievals into the LLM responses. We evaluate our method on knowledge intensive question answering (QA) tasks and show that our method teaches LLMs to properly handle in-context retrievals and abstain from questions it will likely get wrong. Compared to conventional RA-IT methods, our method prevents model degradation in non-RAG settings while exhibiting superior QA performance.
Eliciting Critical Reasoning in Retrieval-Augmented Language Models via Contrastive Explanations
Idiap Research Institute（Idiap研究机构）；Department of Computer Science, University of Manchester（曼彻斯特大学计算机科学系）；National Biomarker Centre (NBC), CRUK Manchester Institute（国家生物标志物中心（NBC），CRUK曼彻斯特研究所）
Sentence-Level Context Sensitivity as a Training-Free Detector of Unsupported Content, Evaluated Against Trained Verifiers
Centre for Intelligent Cloud Computing, CoE for Advanced Cloud, Faculty of Information Science and Technology, Multimedia University（智能云计算中心、高级云研究中心、信息科学与技术学院、多媒体大学）
Training-Free BioNEL-RAG: Advancing Biomedical Entity Linking through Retrieval-Augmented Generation and Large Language Model
University of Science and Technology of China（中国科学技术大学）；School of Biomedical Engineering（生物医学工程学院）；Division of Life Sciences and Medicine（生命科学与医学部）；Department of Precision Machinery and Precision Instruments（精密机械与精密仪器系）
Unsupervised Information Refinement Training of Large Language Models for Retrieval-Augmented Generation
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）；Pattern Recognition Center, WeChat AI（微信AI模式识别中心）
KARE-RAG: Knowledge-Aware Refinement and Enhancement for RAG
School of Biomedical Engineering, Tsinghua, China（清华大学生物医学工程学院）；School of Informatics, Xiamen University, China（厦门大学信息学院）；Department of Computer Science and Technology, Institute for AI, Tsinghua University, China（清华大学计算机科学与技术系）；Dept. of Computer Science and Technology, Northeastern University, Shenyang, China（东北大学沈阳校区计算机科学与技术系）；Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China（中国科学院信息工程研究所）
DACL-RAG: Data Augmentation Strategy with Curriculum Learning for Retrieval-Augmented Generation
School of Information Science and Technology, University of Science and Technology of China（信息科学与技术学院，中国科学技术大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（人工智能研究院，合肥综合性国家科学中心）
Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning
McGill University \& Mila Montréal QC Canada；The Chinese University of Hong Kong Hong Kong China；Huawei Noah’s Ark Lab Montréal QC Canada；University of Manitoba Winnipeg MB Canada；Tianjin University Tianjin China；Independent Researcher Hong Kong China；McGill University \& Mila；The Chinese University of Hong Kong；Huawei Noah’s Ark Lab；University of Manitoba；Tianjin University；Independent Researcher
RAG-HAR: Retrieval Augmented Generation-based Human Activity Recognition
University of Sydney Australia（悉尼大学澳大利亚分校）；University of Sri Jayewardenepura Sri Lanka（Sri Jayewardenepura大学 Sri Lanka）；Curtin University Australia（Curtin大学澳大利亚分校）；Colorado State University USA（科罗拉多州立大学美国分校）
