Zhi Rui Tam — accepted AI-conference papers

2026

ImagenWorld: Stress-Testing Image Generation Models with Explainable Human Evaluation on Open-ended Real-World Tasks

Samin Mahdizadeh Sani, Max Ku, Nima Jamali, Matina Mahdizadeh Sani, Paria Khoshtab, Wei-Chieh Sun +20

ICLR 2026poster

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, existing benchmarks remain limited, either focus on isolated tasks, cover only narrow domains, or provide opaque scores…

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2025

Mitigating Forgetting in LLM Fine-Tuning via Low-Perplexity Token Learning

Chao-Chung Wu, Zhi Rui Tam, Chieh-Yen Lin, Yun-Nung Chen, Shao-Hua Sun, Hung-yi Lee

NeurIPS 2025poster

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 impact on cross-domain generalization remains poorly understood. This paper presents a systematic analysis revealing th…

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2025

None of the Above, Less of the Right Parallel Patterns in Human and LLM Performance on Multi-Choice Questions Answering

Zhi Rui Tam, Cheng-Kuang Wu, Chieh-Yen Lin, Yun-Nung Chen

ACL 2025finding

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 assess true knowledge. However, their impact on Large Language Models (LLMs) evaluation remains underexplored. Through syste…

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2024

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, Chieh-Yen Lin, Hung-yi Lee, Yun-Nung Chen

EMNLP 2024main

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 investigate whether LLMs can determine when to request help under varying information availability. Our experiments show that wit…

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2024

Let Me Speak Freely? A Study On The Impact Of Format Restrictions On Large Language Model Performance.

Zhi Rui Tam, Cheng-Kuang Wu, Yi-Lin Tsai, Chieh-Yen Lin, Hung-yi Lee, Yun-Nung Chen

EMNLP 2024industry

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 from large language models (LLMs).This study investigates whether such constraints on generation space impact LLMs’ abili…

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2024

StreamBench: Towards Benchmarking Continuous Improvement of Language Agents

Cheng-Kuang Wu, Zhi Rui Tam, Chieh-Yen Lin, Yun-Nung Chen, Hung-yi Lee

NeurIPS 2024poster

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-deployment. However, existing benchmarks primarily evaluate their innate capabilities and do not assess their ability to improv…

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2023

OpenAssistant Conversations - Democratizing Large Language Model Alignment

Andreas Köpf, Yannic Kilcher, Dimitri von Rütte, Sotiris Anagnostidis, Zhi Rui Tam, Keith Stevens +12

NeurIPS 2023oral

Aligning large language models (LLMs) with human preferences has proven to drastically improve usability and has driven rapid adoption as demonstrated by ChatGPT. Alignment techniques such as supervised fine-tuning (\textit{SFT}) and reinforcement learning from human feedback (\textit{RLHF}) greatl…

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2020

Character-Preserving Coherent Story Visualization

Yun-Zhu Song, Zhi Rui Tam, Hung-Jen Chen, Huiao-Han Lu, Hong-Han Shuai

ECCV 2020poster

Story visualization aims at generating a sequence of images to narrate each sentence in a multi-sentence story. Different from video generation that focuses on maintaining the continuity of generated images (frames), story visualization emphasizes preserving the global consistency of characters and…

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