4 papers
Learning to Refine Hidden States for Reliable LLM Reasoning
Chia-Hsuan Hsu, Jui-Ming Yao
Large language models show strong reasoning ability, but their internal reasoning process can remain unstable in complex multi-step settings, where early hidden-state errors may pr…
Multi-TW: Benchmarking Multimodal Models on Traditional Chinese Question Answering in Taiwan
Jui-Ming Yao, Bing-Cheng Xie, Sheng-Wei Peng +5
Multimodal Large Language Models (MLLMs) process visual, acoustic, and textual inputs, addressing the limitations of single-modality LLMs. However, existing benchmarks often overlo…
Token Constraint Decoding Improves Robustness on Question Answering for Large Language Models
Jui-Ming Yao, Hao-Yuan Chen, Zi-Xian Tang +4
Large Language Models (LLMs) have demonstrated impressive performance on multiple-choice question answering (MCQA) benchmarks, yet they remain highly vulnerable to minor input pert…
Verbal Process Supervision Elicits Better Coding Agents
Hao-Yuan Chen, Cheng-Pong Huang, Jui-Ming Yao
The emergence of large language models and their applications as AI agents have significantly advanced state-of-the-art code generation benchmarks, transforming modern software eng…