1 citations · 2 across the 5 of their papers we have counts for
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Mitigating Package Hallucinations in Large Language Models via Model Editing
Shuhan Liu, Yukai Zhao, Xing Hu +3
Large language models (LLMs) have demonstrated strong capabilities in software engineering tasks, such as code generation, library recommendation, and dependency configuration. How…
RACE-Bench: A Reasoning-Augmented Benchmark for Repository-Level Code Agents on Feature Addition
Shuhan Liu, Zhiyi Zhao, Xing Hu +3
Repository-level code agents have shown strong promise in real-world feature addition tasks, making reliable evaluation of their capabilities increasingly important. However, exist…
SEER: Self-Enhancing Chain-of-Thought Compression for Reasoning Models
Kerui Huang, Shuhan Liu, Xing Hu +3
Chain-of-Thought (CoT) prompting can substantially improve the reasoning ability of large language models (LLMs), but it often comes with high inference cost due to long and poorly…
CREME: Robustness Enhancement of Code LLMs via Layer-Aware Model Editing
Shuhan Liu, Xing Hu, Kerui Huang +3
Large language models (LLMs) have demonstrated impressive capabilities in code generation, where the natural language prompt plays a crucial role in conveying user intent to the mo…