Yongbin Li
7 papers in the PaperMetrix corpus
Papers by this author
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CATS: A Pragmatic Chinese Answer-to-Sequence Dataset with Large Scale and High Quality
2023
Liang Li, Ruiying Geng, Chengyang Fang, Bing Li, Can Ma, Rongyu Cao, Binhua Li, Fei Huang, Yongbin Li. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.
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Exploring Large Language Models for Multi-Modal Out-of-Distribution Detection
2023
Out-of-distribution (OOD) detection is essential for reliable and trustworthy machine learning. Recent multi-modal OOD detection leverages textual information from in-distribution (ID) class names for visual OOD detection, yet it currently neglects the rich contextual information …
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Extend Model Merging from Fine-Tuned to Pre-Trained Large Language Models via Weight Disentanglement
2024 · arXiv (Cornell University)
Merging Large Language Models (LLMs) aims to amalgamate multiple homologous LLMs into one with all the capabilities. Ideally, any LLMs sharing the same backbone should be mergeable, irrespective of whether they are Fine-Tuned (FT) with …
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On the Role of Attention Heads in Large Language Model Safety
2024 · arXiv (Cornell University)
Large language models (LLMs) achieve state-of-the-art performance on multiple language tasks, yet their safety guardrails can be circumvented, leading to harmful generations. In light of this, recent research on safety mechanisms has emerged, revealing that …
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ExploraCoder: Advancing code generation for multiple unseen APIs via planning and chained exploration
2024 · arXiv (Cornell University)
Large language models face intrinsic limitations in coding with APIs that are unseen in their training corpora. As libraries continuously evolve, it becomes impractical to exhaustively retrain LLMs with new API knowledge. This limitation hampers …
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SWE-GPT: A Process-Centric Language Model for Automated Software Improvement
2025 · Proceedings of the ACM on software engineering.
Large language models (LLMs) have demonstrated remarkable performance in code generation, significantly enhancing the coding efficiency of developers. Recent advancements in LLM-based agents have led to significant progress in end-to-end automatic software engineering (ASE), particularly …
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Thinking Longer, Not Larger: Enhancing Software Engineering Agents via Scaling Test-Time Compute
2025 · arXiv (Cornell University)
Recent advancements in software engineering agents have demonstrated promising capabilities in automating program improvements. However, their reliance on closed-source or resource-intensive models introduces significant deployment challenges in private environments, prompting a critical question: \textit{How can …