13 citations · 22 across the 11 of their papers we have counts for
13 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…
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…
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…
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…
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…
Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach
Chaoxu Pang, Yixuan Cao, Ganbin Zhou +2
Numerical consistency across tables in disclosure documents is critical for ensuring accuracy, maintaining credibility, and avoiding reputational and economic risks. Automated tabu…
Attention with Dependency Parsing Augmentation for Fine-Grained Attribution
Qiang Ding, Lvzhou Luo, Yixuan Cao +1
To assist humans in efficiently validating RAG-generated content, developing a fine-grained attribution mechanism that provides supporting evidence from retrieved documents for eve…
Uncovering Limitations of Large Language Models in Information Seeking from Tables
Chaoxu Pang, Yixuan Cao, Chunhao Yang +1
Tables are recognized for their high information density and widespread usage, serving as essential sources of information. Seeking information from tables (TIS) is a crucial capab…
Guideline Learning for In-context Information Extraction
Chaoxu Pang, Yixuan Cao, Qiang Ding +1
Large language models (LLMs) can perform a new task by merely conditioning on task instructions and a few input-output examples, without optimizing any parameters. This is called I…
Extracting Variable-Depth Logical Document Hierarchy from Long Documents: Method, Evaluation, and Application
Rongyu Cao, Yixuan Cao, Ganbin Zhou +1
In this paper, we study the problem of extracting variable-depth "logical document hierarchy" from long documents, namely organizing the recognized "physical document objects" into…