135 citations · 145 across the 17 of their papers we have counts for
20 papers
Adapting Generative Recommenders for Multi-Turn Interaction
Yu-Chen Den, Zhi Rui Tam, Yung-Yu Shih +4
Generative recommenders decode items from a user's interaction history, but offer no way for users to correct a recommendation that misses their current intent. Adding conversation…
Transfer: Coefficient Transfer for Efficient Model Merging
Shih-Cheng Huang, Zhi Rui Tam, Chieh-Yen Lin +3
Model merging offers a promising solution for combining multiple fine-tuned checkpoints into a single model through parameter arithmetic. However, finding optimal merging coefficie…
TasteRoute: Personalized Routing for Video Generation
Zhi Rui Tam, Chao-Chung Wu, Sin-Han Yang +8
Rapid progress in video generation has led to a plethora of models that differ substantially in capability and generation cost. This raises a natural question: can each request be…
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…
ICICLE: Expanding Retrieval with In-Context Documents
Yu-Chen Den, Yung-Yu Shih, Zhi Rui Tam +4
Generative retrieval (GR) maps queries directly to document identifiers (docids) using parametric knowledge, However, this design makes corpus expansion costly: adding new document…
ImagenWorld: Stress-Testing Image Generation Models with Explainable Human Evaluation on Open-ended Real-World Tasks
Samin Mahdizadeh Sani, Max Ku, Nima Jamali +23
Advances in diffusion, autoregressive, and hybrid models have enabled high-quality image synthesis for tasks such as text-to-image, editing, and reference-guided composition. Yet,…
Expected Harm: Rethinking Safety Evaluation of (Mis)Aligned LLMs
Yen-Shan Chen, Zhi Rui Tam, Cheng-Kuang Wu +1
Current evaluations of LLM safety predominantly rely on severity-based taxonomies to assess the harmfulness of models' responses to malicious queries. We argue that this formulatio…
MedVoiceBias: A Controlled Study of Audio LLM Behavior in Clinical Decision-Making
Zhi Rui Tam, Yun-Nung Chen
As large language models transition from text-based interfaces to audio interactions in clinical settings, they might introduce new vulnerabilities through paralinguistic cues in a…
Global PIQA: Evaluating Commonsense Reasoning Across 100+ Languages and Cultures
Tyler A. Chang, Catherine Arnett, Abdelrahman Sadallah +377
To date, there exist almost no culturally-specific evaluation benchmarks for large language models (LLMs) that cover a large number of languages and cultures. In this paper, we pre…
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…
VisTW: Benchmarking Vision-Language Models for Traditional Chinese in Taiwan
Zhi Rui Tam, Ya-Ting Pai, Yen-Wei Lee +1
In this paper, we propose a comprehensive evaluation benchmark for Visual Language Models (VLM) in Traditional Chinese. Our evaluation suite, the first of its kind, contains two co…
None of the Above, Less of the Right: Parallel Patterns between Humans and LLMs on Multi-Choice Questions Answering
Zhi Rui Tam, Cheng-Kuang Wu, Chieh-Yen Lin +1
Multiple-choice exam questions with "None of the above" (NA) options have been extensively studied in educational testing, in which existing research suggests that they better asse…
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…
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…
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…
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…
An Improved Traditional Chinese Evaluation Suite for Foundation Model
Zhi-Rui Tam, Ya-Ting Pai, Yen-Wei Lee +4
We present TMMLU+, a new benchmark designed for Traditional Chinese language understanding. TMMLU+ is a multi-choice question-answering dataset with 66 subjects from elementary to…
OpenAssistant Conversations -- Democratizing Large Language Model Alignment
Andreas Köpf, Yannic Kilcher, Dimitri von Rütte +15
Aligning large language models (LLMs) with human preferences has proven to drastically improve usability and has driven rapid adoption as demonstrated by ChatGPT. Alignment techniq…
Gradient Normalization for Generative Adversarial Networks
Yi-Lun Wu, Hong-Han Shuai, Zhi-Rui Tam +1
In this paper, we propose a novel normalization method called gradient normalization (GN) to tackle the training instability of Generative Adversarial Networks (GANs) caused by the…