11 papers · 1 filter
Rethinking Dense Sequential Chains: Reasoning Language Models Can Extract Answers from Sparse, Order-Shuffling Chain-of-Thoughts
Yi-Chang Chen, Feng-Ting Liao, Da-shan Shiu +1
Modern reasoning language models generate dense, sequential chain-of-thought traces implicitly assuming that every token contributes and that steps must be consumed in order. We ch…
ReMedi: Reasoner for Medical Clinical Prediction
Yushi Cao, Yiming Chen, Hongchao Jiang +2
Predicting future clinical outcomes from electronic health records (EHR) remains challenging due to the complexity and heterogeneity of patient data. LLMs have shown strong potenti…
On Calibration of Large Language Models: From Response To Capability
Sin-Han Yang, Cheng-Kuang Wu, Chieh-Yen Lin +3
Large language models (LLMs) are widely deployed as general-purpose problem solvers, making accurate confidence estimation critical for reliable use. Prior work on LLM calibration…
Mitigating Forgetting in LLM Fine-Tuning via Low-Perplexity Token Learning
Chao-Chung Wu, Zhi Rui Tam, Chieh-Yen Lin +3
Maintaining consistent model performance across domains is a fundamental challenge in machine learning. While recent work has explored using LLM-generated data for fine-tuning, its…
Answer, Refuse, or Guess? Investigating Risk-Aware Decision Making in Language Models
Cheng-Kuang Wu, Zhi Rui Tam, Chieh-Yen Lin +2
Language models (LMs) are increasingly used to build agents that can act autonomously to achieve goals. During this automatic process, agents need to take a series of actions, some…
Language Matters: How Do Multilingual Input and Reasoning Paths Affect Large Reasoning Models?
Zhi Rui Tam, Cheng-Kuang Wu, Yu Ying Chiu +3
Large reasoning models (LRMs) have demonstrated impressive performance across a range of reasoning tasks, yet little is known about their internal reasoning processes in multilingu…