Papers by Yixuan Cao
AttnComp: Attention-Guided Adaptive Context Compression for Retrieval-Augmented Generation (2025.findings-emnlp)
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| Challenge: | Existing methods for augmented large language models suffer from irrelevant retrieved content . existing methods struggle to adapt compression rates for different context, maintain low latency . |
| Approach: | We propose an adaptive, efficient and context-aware compression framework to reduce retrieved content . AttnComp uses a top-p compression algorithm to retain the minimal set of documents whose attention weights exceed a threshold. |
| Outcome: | Experiments show that AttnComp outperforms existing compression methods and uncompressed baselines in achieving higher accuracy with substantial compression rates and lower latency. |
Uncovering Limitations of Large Language Models in Information Seeking from Tables (2024.findings-acl)
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| Challenge: | Existing benchmarks for Table Information Seeking (TabIS) are lacking in reliable evaluation. |
| Approach: | They propose a benchmark to evaluate the table information seeking abilities of large language models . they use a single-choice question format instead of a text-based evaluation . |
| Outcome: | The proposed benchmark is more reliable than existing models and is available online. |
Guideline Learning for In-Context Information Extraction (2023.emnlp-main)
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| Challenge: | Large language models can perform a task by conditioning on task instructions and a few input-output examples without optimizing any parameters. |
| Approach: | They propose a guideline learning framework for In-context IE which reflectively learns and follows guidelines. |
| Outcome: | The proposed framework improves the performance of in-context IE by synthesizing and following guidelines. |
EA-Agent: A Structured Multi-Step Reasoning Agent for Entity Alignment (2026.acl-long)
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| Challenge: | Entity alignment (EA) aims to identify entities across different knowledge graphs (KGs) that refer to the same real-world object. |
| Approach: | They propose to use large language models to integrate semantic knowledge into EA to identify entities across different knowledge graphs that refer to the same object. |
| Outcome: | The proposed agent outperforms existing methods and achieves state-of-the-art performance on three benchmark datasets. |
Attention with Dependency Parsing Augmentation for Fine-Grained Attribution (2025.findings-acl)
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| Challenge: | Existing fine-grained attribution methods rely on model-internal similarity metrics but lack a fine-grain representation of the data. |
| Approach: | They propose to use model-internal similarity metrics to validate RAG-generated content . they aggregate token-wise evidence through set union operations and integrate dependency parsing to enrich the semantic completeness of target spans. |
| Outcome: | The proposed method outperforms all prior works in the validation of RAG-generated content. |
Dialogue Response Selection with Hierarchical Curriculum Learning (2021.acl-long)
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Yixuan Su, Deng Cai, Qingyu Zhou, Zibo Lin, Simon Baker, Yunbo Cao, Shuming Shi, Nigel Collier, Yan Wang
| Challenge: | Empirical studies on three benchmark datasets with three state-of-the-art matching models demonstrate that the proposed learning framework significantly improves the model performance across various evaluation metrics. |
| Approach: | They propose a hierarchical curriculum learning framework that trains matching models in an “easy-to-difficult” scheme. |
| Outcome: | The proposed framework significantly improves the model performance across evaluation metrics on three benchmark datasets with three state-of-the-art matching models. |
Navigating Large-Scale Document Collections: MuDABench for Multi-Document Analytical QA (2026.findings-acl)
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| Challenge: | Existing multi-document QA benchmarks require information from only a few documents with limited cross-document reasoning. |
| Approach: | They propose a benchmark for multi-document analytical QA that extracts and synthesizes information across multiple documents to perform quantitative analysis. |
| Outcome: | The proposed approach improves both process and outcome metrics but still has bottlenecks compared to human experts. |