5 papers
CoCR-RAG: Enhancing Retrieval-Augmented Generation in Web Q&A via Concept-oriented Context Reconstruction
Kaize Shi, Xueyao Sun, Qika Lin +4
Retrieval-augmented generation (RAG) has shown promising results in enhancing Q&A by incorporating information from the web and other external sources. However, the supporting docu…
Concept than Document: Context Compression via AMR-based Conceptual Entropy
Kaize Shi, Xueyao Sun, Xiaohui Tao +3
Large Language Models (LLMs) face information overload when handling long contexts, particularly in Retrieval-Augmented Generation (RAG) where extensive supporting documents often…
LLaMA-E: Empowering E-commerce Authoring with Object-Interleaved Instruction Following
Kaize Shi, Xueyao Sun, Dingxian Wang +3
E-commerce authoring entails creating engaging, diverse, and targeted content to enhance preference elicitation and retrieval experience. While Large Language Models (LLMs) have re…
Expert-Guided Extinction of Toxic Tokens for Debiased Generation
Xueyao Sun, Kaize Shi, Haoran Tang +2
Large language models (LLMs) can elicit social bias during generations, especially when inference with toxic prompts. Controlling the sensitive attributes in generation encounters…
Compressing Long Context for Enhancing RAG with AMR-based Concept Distillation
Kaize Shi, Xueyao Sun, Qing Li +1
Large Language Models (LLMs) have made significant strides in information acquisition. However, their overreliance on potentially flawed parametric knowledge leads to hallucination…