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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…
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…
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…