35 citations · 37 across the 15 of their papers we have counts for
17 papers
Enhancing Accessibility of Medical Texts through Large Language Model-Driven Plain Language Adaptation
Ting-Wei Chang, Hen-Hsen Huang, Hsin-Hsi Chen
This paper addresses the challenge of making complex healthcare information more accessible through automated Plain Language Adaptation (PLA). PLA aims to simplify technical medica…
Strategy-Induct: Task-Level Strategy Induction for Instruction Generation
Po-Chun Chen, Hen-Hsen Huang, Hsin-Hsi Chen
Designing effective task-level prompts is crucial for improving the performance of Large Language Models (LLMs). While prior work on instruction induction demonstrates that LLMs ca…
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…
Diverge to Induce Prompting: Multi-Rationale Induction for Zero-Shot Reasoning
Po-Chun Chen, Hen-Hsen Huang, Hsin-Hsi Chen
To address the instability of unguided reasoning paths in standard Chain-of-Thought prompting, recent methods guide large language models (LLMs) by first eliciting a single reasoni…
Visual Lifelog Retrieval through Captioning-Enhanced Interpretation
Yu-Fei Shih, An-Zi Yen, Hen-Hsen Huang +1
People often struggle to remember specific details of past experiences, which can lead to the need to revisit these memories. Consequently, lifelog retrieval has emerged as a cruci…
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…
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…
Pre-Finetuning with Impact Duration Awareness for Stock Movement Prediction
Chr-Jr Chiu, Chung-Chi Chen, Hen-Hsen Huang +1
Understanding the duration of news events' impact on the stock market is crucial for effective time-series forecasting, yet this facet is largely overlooked in current research. Th…
Enhancing Investment Opinion Ranking through Argument-Based Sentiment Analysis
Chung-Chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen +3
In the era of rapid Internet and social media platform development, individuals readily share their viewpoints online. The overwhelming quantity of these posts renders comprehensiv…
Unveiling Selection Biases: Exploring Order and Token Sensitivity in Large Language Models
Sheng-Lun Wei, Cheng-Kuang Wu, Hen-Hsen Huang +1
In this paper, we investigate the phenomena of "selection biases" in Large Language Models (LLMs), focusing on problems where models are tasked with choosing the optimal option fro…
Fidelity-Enriched Contrastive Search: Reconciling the Faithfulness-Diversity Trade-Off in Text Generation
Wei-Lin Chen, Cheng-Kuang Wu, Hsin-Hsi Chen +1
In this paper, we address the hallucination problem commonly found in natural language generation tasks. Language models often generate fluent and convincing content but can lack c…
NumHG: A Dataset for Number-Focused Headline Generation
Jian-Tao Huang, Chung-Chi Chen, Hen-Hsen Huang +1
Headline generation, a key task in abstractive summarization, strives to condense a full-length article into a succinct, single line of text. Notably, while contemporary encoder-de…
Numeral Understanding in Financial Tweets for Fine-grained Crowd-based Forecasting
Chung-Chi Chen, Hen-Hsen Huang, Yow-Ting Shiue +1
Numerals that contain much information in financial documents are crucial for financial decision making. They play different roles in financial analysis processes. This paper is ai…