Papers by Yufan Zhuang

2 papers
Self-Taught Agentic Long Context Understanding (2025.acl-long)

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Challenge: Extensive experiments across seven long-context tasks demonstrate that AgenticLU significantly outperforms state-of-the-art prompting methods and specialized long-consumer LLMs.
Approach: They propose a framework to enhance an LLM's understanding of long-context questions by integrating targeted self-clarification with contextual grounding within an agentic workflow.
Outcome: The proposed framework outperforms state-of-the-art prompting methods and specialized long-context LLMs in seven long-constitut tasks.
Data Contamination Can Cross Language Barriers (2024.emnlp-main)

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Challenge: Existing methods to detect contamination of public benchmarks are too superficial to reflect deeper forms of contamination.
Approach: They propose generalization-based approaches to unmask a cross-lingual form of contamination that inflates LLMs’ performance while evading current detection methods.
Outcome: The proposed model outperforms existing detection methods while avoiding contamination of public benchmarks in the pre-training data.

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