2 citations · 2 across the 14 of their papers we have counts for
10 papers · 1 filter
Personalized Graph-Empowered Large Language Model for Proactive Information Access
Chia Cheng Chang, An-Zi Yen, Hen-Hsen Huang +1
Since individuals may struggle to recall all life details and often confuse events, establishing a system to assist users in recalling forgotten experiences is essential. While num…
Do Before You Judge: Self-Reference as a Pathway to Better LLM Evaluation
Wei-Hsiang Lin, Sheng-Lun Wei, Hen-Hsen Huang +1
LLM-as-Judge frameworks are increasingly popular for AI evaluation, yet research findings on the relationship between models' generation and judgment abilities remain inconsistent.…
Diagnosing Model Editing via Knowledge Spectrum
Tsung-Hsuan Pan, Chung-Chi Chen, Hen-Hsen Huang +1
Model editing, the process of efficiently modifying factual knowledge in pre-trained language models, is critical for maintaining their accuracy and relevance. However, existing ed…
Evaluating Large Language Models as Expert Annotators
Yu-Min Tseng, Wei-Lin Chen, Chung-Chi Chen +1
Textual data annotation, the process of labeling or tagging text with relevant information, is typically costly, time-consuming, and labor-intensive. While large language models (L…
Are Expert-Level Language Models Expert-Level Annotators?
Yu-Min Tseng, Wei-Lin Chen, Chung-Chi Chen +1
Data annotation refers to the labeling or tagging of textual data with relevant information. A large body of works have reported positive results on leveraging LLMs as an alternati…
"Why" Has the Least Side Effect on Model Editing
Tsung-Hsuan Pan, Chung-Chi Chen, Hen-Hsen Huang +1
Training large language models (LLMs) from scratch is an expensive endeavor, particularly as world knowledge continually evolves. To maintain relevance and accuracy of LLMs, model…