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Yixuan Cao

7 accepted papers

2025

Attention with Dependency Parsing Augmentation for Fine-Grained Attribution

ACL 2025finding

To assist humans in efficiently validating RAG-generated content, developing a fine-grained attribution mechanism that provides supporting evidence from retrieved documents for every answer span is essential. Existing fine-grained attribution methods rely on model-internal similarity metrics between…

Cited by 0SourcePDFScholar
2025

AttnComp: Attention-Guided Adaptive Context Compression for Retrieval-Augmented Generation

EMNLP 2025

Retrieval-augmented generation improves the factual accuracy of Large Language Models (LLMs) by incorporating external context, but often suffers from irrelevant retrieved content that hinders effectiveness. Context compression addresses this issue by filtering out irrelevant information from contex

Cited by 0SourcePDFScholar
2025

DETree: DEtecting Human-AI Collaborative Texts via Tree-Structured Hierarchical Representation Learning

NeurIPS 2025poster

Detecting AI-involved text is essential for combating misinformation, plagiarism, and academic misconduct. However, AI text generation includes diverse collaborative processes (AI-written text edited by humans, human-written text edited by AI, and AI-generated text refined by other AI), where vario…

Cited by 0SourcecodeScholar
2024

Uncovering Limitations of Large Language Models in Information Seeking from Tables

ACL 2024findings

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 capability for Large Language Models (LLMs), serving as the foundation of knowledge-based Q&A systems. However, this field pr…

2023

Top-Ambiguity Samples Matter: Understanding Why Deep Ensemble Works in Selective Classification

NeurIPS 2023poster

Selective classification allows a machine learning model to reject some hard inputs and thus improve the reliability of its predictions. In this area, the ensemble method is powerful in practice, but there has been no solid analysis on why the ensemble method works. Inspired by an interesting empiri…

Cited by 2SourcePDFScholar
2021

A Bottom-Up DAG Structure Extraction Model for Math Word Problems

AAAI 2021technical

Research on automatically solving mathematical word problems (MWP) has a long history. Most recent works adopt Seq2Seq approach to predict the result equations as a sequence of quantities and operators. Although result equations can be written as a sequence, it is essentially a structure. More preci…

Cited by 58SourcePDFScholar