6 citations · 6 across the 9 of their papers we have counts for
9 papers
Joint Optimization of Tool Creation and Use for Large Language Model Agents
Zhi Rui Tam, Chieh-Yen Lin, Yun-Nung Chen +2
Tool-augmented language models are bounded by the APIs humans bothered to write; existing tool-creation systems patch this by prompting a frozen LLM at inference time, leaving the…
Findings of the First Teaching Monster Challenge: A Benchmark of Pedagogical Content Knowledge in AI Agents
Yi-Cheng Lin, Yu-Kai Guo, Szu-Chi Chen +15
AI agents can now solve problems, answer like subject experts, and generate long-form multimodal content. However, whether they can adapt a lesson to fit a specified learner, which…
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…
BILLY: Steering Large Language Models via Merging Persona Vectors for Creative Generation
Tsung-Min Pai, Jui-I Wang, Li-Chun Lu +3
Multi-LLM systems enhance the creativity of large language models by simulating human collective intelligence but suffer from significant drawbacks, such as high computational cost…
Game-Time: Evaluating Temporal Dynamics in Spoken Language Models
Kai-Wei Chang, En-Pei Hu, Chun-Yi Kuan +7
Conversational Spoken Language Models (SLMs) are emerging as a promising paradigm for real-time speech interaction. However, their capacity of temporal dynamics, including the abil…
Rethinking Creativity Evaluation: A Critical Analysis of Existing Creativity Evaluations
Li-Chun Lu, Miri Liu, Pin-Chun Lu +3
We examine, analyze, and compare four representative creativity measures--perplexity, LLM-as-a-Judge, the Creativity Index (CI; measuring n-gram overlap with web corpora), and synt…
Adaptive Helpfulness-Harmlessness Alignment with Preference Vectors
Ren-Wei Liang, Chin-Ting Hsu, Chan-Hung Yu +6
Ensuring that large language models (LLMs) are both helpful and harmless is a critical challenge, as overly strict constraints can lead to excessive refusals, while permissive mode…
LLM Discussion: Enhancing the Creativity of Large Language Models via Discussion Framework and Role-Play
Li-Chun Lu, Shou-Jen Chen, Tsung-Min Pai +3
Large language models (LLMs) have shown exceptional proficiency in natural language processing but often fall short of generating creative and original responses to open-ended ques…
REBORN: Reinforcement-Learned Boundary Segmentation with Iterative Training for Unsupervised ASR
Liang-Hsuan Tseng, En-Pei Hu, Cheng-Han Chiang +4
Unsupervised automatic speech recognition (ASR) aims to learn the mapping between the speech signal and its corresponding textual transcription without the supervision of paired sp…