4 citations · 5 across the 7 of their papers we have counts for
7 papers
-dLLM: Accelerating Diffusion Large Language Models via Spatio-Temporal Redundancy Reduction
Zhenbang Du, Kejing Xia, Xinrui Zhong +6
Diffusion Large Language Models (dLLMs) have emerged as a promising alternative to autoregressive generation by enabling parallel token prediction. However, practical dLLM decoding…
Learning in the Fisher Subspace: A Guided Initialization for LoRA Fine-Tuning
Zhi-Quan Feng, Ying-Jia Lin, Hung-Yu Kao
LoRA adapts large language models (LLMs) by restricting updates to low-rank subspaces of pre-trained weights. While this substantially reduces training cost, the effectiveness of a…
PromptRad: Knowledge-Enhanced Multi-Label Prompt-Tuning for Low-Resource Radiology Report Labeling
Ying-Jia Lin, Tzu-Chin Lo, Ping-Chien Li +3
Automatic report labeling facilitates the identification of clinical findings from unstructured text and enables large-scale annotation for medical imaging research. Existing rule-…
SCURank: Ranking Multiple Candidate Summaries with Summary Content Units for Enhanced Summarization
Bo-Jyun Wang, Ying-Jia Lin, Hung-Yu Kao
Small language models (SLMs), such as BART, can achieve summarization performance comparable to large language models (LLMs) via distillation. However, existing LLM-based ranking s…
From Persona to Person: Enhancing the Naturalness with Multiple Discourse Relations Graph Learning in Personalized Dialogue Generation
Chih-Hao Hsu, Ying-Jia Lin, Hung-Yu Kao
In dialogue generation, the naturalness of responses is crucial for effective human-machine interaction. Personalized response generation poses even greater challenges, as the resp…
MAPLE: Enhancing Review Generation with Multi-Aspect Prompt LEarning in Explainable Recommendation
Ching-Wen Yang, Zhi-Quan Feng, Ying-Jia Lin +5
The Explainable Recommendation task is designed to receive a pair of user and item and output explanations to justify why an item is recommended to a user. Many models approach rev…
CFEVER: A Chinese Fact Extraction and VERification Dataset
Ying-Jia Lin, Chun-Yi Lin, Chia-Jen Yeh +5
We present CFEVER, a Chinese dataset designed for Fact Extraction and VERification. CFEVER comprises 30,012 manually created claims based on content in Chinese Wikipedia. Each clai…