most citedNovel Word Embedding and Translation-based Language Modeling for Extractive Speech Summarization

1 citations · 1 across the 1 of their papers we have counts for

collaborators
Showing cs.CLShow all

9 papers · 1 filter

cs.CL2025

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…

cs.CL2025

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…

cs.CL20242 cited

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…

cs.CL2023

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…

cs.CL2023

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…

cs.CL20239 cited

Large Language Models Perform Diagnostic Reasoning

Cheng-Kuang Wu, Wei-Lin Chen, Hsin-Hsi Chen

We explore the extension of chain-of-thought (CoT) prompting to medical reasoning for the task of automatic diagnosis. Motivated by doctors' underlying reasoning process, we presen…