7 papers · 1 filter
The Gray Zone of Faithfulness: Taming Ambiguity in Unfaithfulness Detection
Qiang Ding, Lvzhou Luo, Yixuan Cao +1
Ensuring that Large Language Models (LLMs) generate summaries faithful to a given source document is essential for real-world applications. While prior research has explored LLM fa…
Read the Docs Before Rewriting: Equip Rewriter with Domain Knowledge via Continual Pre-training
Qi Wang, Yixuan Cao, Yifan Liu +2
A Retrieval-Augmented Generation (RAG)-based question-answering (QA) system enhances a large language model's knowledge by retrieving relevant documents based on user queries. Disc…
DETree: DEtecting Human-AI Collaborative Texts via Tree-Structured Hierarchical Representation Learning
Yongxin He, Shan Zhang, Yixuan Cao +2
Detecting AI-involved text is essential for combating misinformation, plagiarism, and academic misconduct. However, AI text generation includes diverse collaborative processes (AI-…
Reasoning Pattern Matters: Learning to Reason without Human Rationales
Chaoxu Pang, Yixuan Cao, Ping Luo
Large Language Models (LLMs) have demonstrated remarkable reasoning capabilities under the widely adopted SFT+RLVR paradigm, which first performs Supervised Fine-Tuning (SFT) on hu…
AttnComp: Attention-Guided Adaptive Context Compression for Retrieval-Augmented Generation
Lvzhou Luo, Yixuan Cao, Ping Luo
Retrieval-augmented generation improves the factual accuracy of Large Language Models (LLMs) by incorporating external context, but often suffers from irrelevant retrieved content…
Towards Efficient Quantity Retrieval from Text:An Approach via Description Parsing and Weak Supervision
Yixuan Cao, Zhengrong Chen, Chengxuan Xia +2
Quantitative facts are continually generated by companies and governments, supporting data-driven decision-making. While common facts are structured, many long-tail quantitative fa…