1 citations · 1 across the 2 of their papers we have counts for
5 papers
AutoL2S: Auto Long-Short Reasoning for Efficient Large Language Models
Feng Luo, Yu-Neng Chuang, Guanchu Wang +8
Reasoning-capable large language models (LLMs) achieve strong performance on complex tasks but often exhibit overthinking after distillation, generating unnecessarily long chain-of…
Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models
Yang Sui, Yu-Neng Chuang, Guanchu Wang +9
Large Language Models (LLMs) have demonstrated remarkable capabilities in complex tasks. Recent advancements in Large Reasoning Models (LRMs), such as OpenAI o1 and DeepSeek-R1, ha…
Interpreting and Steering LLMs with Mutual Information-based Explanations on Sparse Autoencoders
Xuansheng Wu, Jiayi Yuan, Wenlin Yao +2
Large language models (LLMs) excel at handling human queries, but they can occasionally generate flawed or unexpected responses. Understanding their internal states is crucial for…
The Science of Evaluating Foundation Models
Jiayi Yuan, Jiamu Zhang, Andrew Wen +1
The emergent phenomena of large foundation models have revolutionized natural language processing. However, evaluating these models presents significant challenges due to their siz…
Taylor Unswift: Secured Weight Release for Large Language Models via Taylor Expansion
Guanchu Wang, Yu-Neng Chuang, Ruixiang Tang +8
Ensuring the security of released large language models (LLMs) poses a significant dilemma, as existing mechanisms either compromise ownership rights or raise data privacy concerns…