10 papers
Navigating Large-Scale Document Collections: MuDABench for Multi-Document Analytical QA
Zhanli Li, Yixuan Cao, Lvzhou Luo +1
This paper introduces the task of analytical question answering over large, semi-structured document collections. We present MuDABench, a benchmark for multi-document analytical QA…
DeepRead: Document Structure-Aware Reasoning to Enhance Agentic Search
Zhanli Li, Huiwen Tian, Lvzhou Luo +2
With the rapid advancement of tool-use capabilities in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) is shifting from static, one-shot retrieval toward autonom…
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…