Papers by Cheryl Lee

2 papers
UniDebugger: Hierarchical Multi-Agent Framework for Unified Software Debugging (2025.emnlp-main)

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Challenge: Existing LLMs focus on isolated steps and struggle with complex bugs.
Approach: They propose a framework for unified debugging through multi-agent synergy . it mimics the entire cognitive processes of developers with each agent specialized as a particular component of this process .
Outcome: The proposed framework outperforms state-of-the-art methods on repo-level benchmarks.
Learning to Ask: When LLM Agents Meet Unclear Instruction (2025.emnlp-main)

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Challenge: Despite their impressive capabilities, LLMs struggle with complex computations and delivering accurate, timely information.
Approach: They propose a framework that prompts LLM agents to ask questions when they encounter obstacles due to unclear instructions and an automated evaluation tool called ToolEvaluator.
Outcome: The proposed framework outperforms existing frameworks for tool learning in the Noisy ToolBench.

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