8 papers · 1 filter
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…
Co-Trained Retriever-Generator Framework for Question Generation in Earnings Calls
Yining Juan, Chung-Chi Chen, Hen-Hsen Huang +1
In diverse professional environments, ranging from academic conferences to corporate earnings calls, the ability to anticipate audience questions stands paramount. Traditional meth…
Enhancing Financial Sentiment Analysis with Expert-Designed Hint
Chung-Chi Chen, Hiroya Takamura, Ichiro Kobayashi +1
This paper investigates the role of expert-designed hint in enhancing sentiment analysis on financial social media posts. We explore the capability of large language models (LLMs)…