3 papers
cs.LG2026
Adaptive Helpfulness-Harmlessness Alignment with Preference Vectors
Ren-Wei Liang, Chin-Ting Hsu, Chan-Hung Yu +6
Ensuring that large language models (LLMs) are both helpful and harmless is a critical challenge, as overly strict constraints can lead to excessive refusals, while permissive mode…
cs.CL2024
Chat Vector: A Simple Approach to Equip LLMs with Instruction Following and Model Alignment in New Languages
Shih-Cheng Huang, Pin-Zu Li, Yu-Chi Hsu +5
Recently, the development of open-source large language models (LLMs) has advanced rapidly. Nevertheless, due to data constraints, the capabilities of most open-source LLMs are pri…
cs.CL2024
Systematic Analysis for Pretrained Language Model Priming for Parameter-Efficient Fine-tuning
Shih-Cheng Huang, Shih-Heng Wang, Min-Han Shih +2
Parameter-efficient (PE) methods (like Prompts or Adapters) for adapting pre-trained language models (PLM) to downstream tasks have been popular recently. However, hindrances still…