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20212026
most citedOpenAssistant Conversations -- Democratizing Large Language Model Alignment

135 citations · 145 across the 17 of their papers we have counts for

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11 papers · 1 filter

cs.CL2025

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…

cs.CL2025

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…

cs.CL2025

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…

cs.CL2025

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…

cs.CL2025

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…

cs.CL2025

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…

cs.CL2024★ 6 cited

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…

cs.CL2024

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…

cs.CL2024

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…

cs.CL2024★ 2 cited

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…

cs.CL2023★ 135 cited

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…