6 citations · 33 across the 36 of their papers we have counts for
24 papers · 1 filter
From Memory to Behavior: A Behavior-Aware Role-Playing Framework for Social Media Influencers
Ji-Lun Peng, Yi-Zhen Zhang, Chun-Nan Chou +1
Large language models have shown strong potential as role-playing agents for real individuals, yet faithful impersonating remains challenging. Existing in-context learning-based me…
Tri-PvP: Exposing Modality Bias in Omni-Modal Large Language Models through Perceptual-Propositional Evidence Conflicts
Yen-Ting Piao, Shu-Yun Chen, Chin-Hui Chu +4
Omni-modal large language models (OLLMs) jointly process vision, audio, and text, yet their modality bias under cross-modal conflict remains underexplored. Existing benchmarks conf…
When Users Don't Ask: Benchmarking Context-Driven Memory Retrieval in Conversational Agents
Wen-Yu Chang, Yun-Nung Chen
Large language models (LLMs) are increas- ingly deployed as long-horizon conversational agents, motivating growing interest in mem- ory systems. However, existing benchmarks primar…
Rethinking Role-Playing Evaluation: Anonymous Benchmarking and a Systematic Study of Personality Effects
Ji-Lun Peng, Yun-Nung Chen
Large Language Models (LLMs) have shown remarkable potential in developing role-playing agents (RPAs). However, current evaluation frameworks rely heavily on well-known fictional c…
AdaSearch: Balancing Parametric Knowledge and Search in Large Language Models via Reinforcement Learning
Tzu-Han Lin, Wei-Lin Chen, Chen-An Li +3
Equipping large language models (LLMs) with search engines via reinforcement learning (RL) promises effective search agents. However, adaptively balancing internal parametric knowl…
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…
Visualizing Dialogues: Enhancing Image Selection through Dialogue Understanding with Large Language Models
Chang-Sheng Kao, Yun-Nung Chen
Recent advancements in dialogue systems have highlighted the significance of integrating multimodal responses, which enable conveying ideas through diverse modalities rather than s…
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…
DogeRM: Equipping Reward Models with Domain Knowledge through Model Merging
Tzu-Han Lin, Chen-An Li, Hung-yi Lee +1
Reinforcement learning from human feedback (RLHF) is a popular strategy for aligning large language models (LLMs) with desired behaviors. Reward modeling is a crucial step in RLHF.…
A Survey of Useful LLM Evaluation
Ji-Lun Peng, Sijia Cheng, Egil Diau +4
LLMs have gotten attention across various research domains due to their exceptional performance on a wide range of complex tasks. Therefore, refined methods to evaluate the capabil…
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…
Editing the Mind of Giants: An In-Depth Exploration of Pitfalls of Knowledge Editing in Large Language Models
Cheng-Hsun Hsueh, Paul Kuo-Ming Huang, Tzu-Han Lin +4
Knowledge editing is a rising technique for efficiently updating factual knowledge in large language models (LLMs) with minimal alteration of parameters. However, recent studies ha…
Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalization
Yu-Min Tseng, Yu-Chao Huang, Teng-Yun Hsiao +4
The concept of persona, originally adopted in dialogue literature, has re-surged as a promising framework for tailoring large language models (LLMs) to specific context (e.g., pers…
Injecting Salesperson's Dialogue Strategies in Large Language Models with Chain-of-Thought Reasoning
Wen-Yu Chang, Yun-Nung Chen
Recent research in dialogue systems and corpora has focused on two main categories: task-oriented (TOD) and open-domain (chit-chat) dialogues. TOD systems help users accomplish spe…
Measuring Taiwanese Mandarin Language Understanding
Po-Heng Chen, Sijia Cheng, Wei-Lin Chen +2
The evaluation of large language models (LLMs) has drawn substantial attention in the field recently. This work focuses on evaluating LLMs in a Chinese context, specifically, for T…
InstUPR : Instruction-based Unsupervised Passage Reranking with Large Language Models
Chao-Wei Huang, Yun-Nung Chen
This paper introduces InstUPR, an unsupervised passage reranking method based on large language models (LLMs). Different from existing approaches that rely on extensive training wi…
Unsupervised Multilingual Dense Retrieval via Generative Pseudo Labeling
Chao-Wei Huang, Chen-An Li, Tsu-Yuan Hsu +2
Dense retrieval methods have demonstrated promising performance in multilingual information retrieval, where queries and documents can be in different languages. However, dense ret…
Taiwan LLM: Bridging the Linguistic Divide with a Culturally Aligned Language Model
Yen-Ting Lin, Yun-Nung Chen
In the realm of language models, the nuanced linguistic and cultural intricacies of Traditional Chinese, as spoken in Taiwan, have been largely overlooked. This paper introduces Ta…
Zero-Shot Dialogue Relation Extraction by Relating Explainable Triggers and Relation Names
Ze-Song Xu, Yun-Nung Chen
Developing dialogue relation extraction (DRE) systems often requires a large amount of labeled data, which can be costly and time-consuming to annotate. In order to improve scalabi…
PLM-ICD: Automatic ICD Coding with Pretrained Language Models
Chao-Wei Huang, Shang-Chi Tsai, Yun-Nung Chen
Automatically classifying electronic health records (EHRs) into diagnostic codes has been challenging to the NLP community. State-of-the-art methods treated this problem as a multi…
Contrastive Learning for Improving ASR Robustness in Spoken Language Understanding
Ya-Hsin Chang, Yun-Nung Chen
Spoken language understanding (SLU) is an essential task for machines to understand human speech for better interactions. However, errors from the automatic speech recognizer (ASR)…
Towards Generating Citation Sentences for Multiple References with Intent Control
Jia-Yan Wu, Alexander Te-Wei Shieh, Shih-Ju Hsu +1
Machine-generated citation sentences can aid automated scientific literature review and assist article writing. Current methods in generating citation text were limited to single c…
TREND: Trigger-Enhanced Relation-Extraction Network for Dialogues
Po-Wei Lin, Shang-Yu Su, Yun-Nung Chen
The goal of dialogue relation extraction (DRE) is to identify the relation between two entities in a given dialogue. During conversations, speakers may expose their relations to ce…
HUMBO: Bridging Response Generation and Facial Expression Synthesis
Shang-Yu Su, Po-Wei Lin, Yun-Nung Chen
Spoken dialogue systems that assist users to solve complex tasks such as movie ticket booking have become an emerging research topic in artificial intelligence and natural language…