9 citations · 20 across the 13 of their papers we have counts for
11 papers · 1 filter
On Calibration of Large Language Models: From Response To Capability
Sin-Han Yang, Cheng-Kuang Wu, Chieh-Yen Lin +3
Accurate confidence estimation is critical for reliable use of large language models (LLMs). Prior work on LLM calibration largely focuses on response-level confidence, which estim…
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…
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…
Let Me Speak Freely? A Study on the Impact of Format Restrictions on Performance of Large Language Models
Zhi Rui Tam, Cheng-Kuang Wu, Yi-Lin Tsai +3
Structured generation, the process of producing content in standardized formats like JSON and XML, is widely utilized in real-world applications to extract key output information f…
I Need Help! Evaluating LLM's Ability to Ask for Users' Support: A Case Study on Text-to-SQL Generation
Cheng-Kuang Wu, Zhi Rui Tam, Chao-Chung Wu +3
This study explores the proactive ability of LLMs to seek user support. We propose metrics to evaluate the trade-off between performance improvements and user burden, and investiga…
Unveiling Selection Biases: Exploring Order and Token Sensitivity in Large Language Models
Sheng-Lun Wei, Cheng-Kuang Wu, Hen-Hsen Huang +1
In this paper, we investigate the phenomena of "selection biases" in Large Language Models (LLMs), focusing on problems where models are tasked with choosing the optimal option fro…
StreamBench: Towards Benchmarking Continuous Improvement of Language Agents
Cheng-Kuang Wu, Zhi Rui Tam, Chieh-Yen Lin +2
Recent works have shown that large language model (LLM) agents are able to improve themselves from experience, which is an important ability for continuous enhancement post-deploym…
Fidelity-Enriched Contrastive Search: Reconciling the Faithfulness-Diversity Trade-Off in Text Generation
Wei-Lin Chen, Cheng-Kuang Wu, Hsin-Hsi Chen +1
In this paper, we address the hallucination problem commonly found in natural language generation tasks. Language models often generate fluent and convincing content but can lack c…
Large Language Models Perform Diagnostic Reasoning
Cheng-Kuang Wu, Wei-Lin Chen, Hsin-Hsi Chen
We explore the extension of chain-of-thought (CoT) prompting to medical reasoning for the task of automatic diagnosis. Motivated by doctors' underlying reasoning process, we presen…
Self-ICL: Zero-Shot In-Context Learning with Self-Generated Demonstrations
Wei-Lin Chen, Cheng-Kuang Wu, Yun-Nung Chen +1
Large language models (LLMs) have exhibited striking in-context learning (ICL) ability to adapt to target tasks with a few input-output demonstrations. For better ICL, different me…
ZARA: Improving Few-Shot Self-Rationalization for Small Language Models
Wei-Lin Chen, An-Zi Yen, Cheng-Kuang Wu +2
Language models (LMs) that jointly generate end-task answers as well as free-text rationales are known as self-rationalization models. Recent works demonstrate great performance ga…