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cs.CL2024
On the Loss of Context-awareness in General Instruction Fine-tuning
Yihan Wang, Andrew Bai, Nanyun Peng +1
Pre-trained Large Language Models (LLMs) require post-training methods such as supervised fine-tuning (SFT) on instruction-response pairs to enable instruction following. However,…
cs.CL2024
CLUE: Concept-Level Uncertainty Estimation for Large Language Models
Yu-Hsiang Wang, Andrew Bai, Che-Ping Tsai +1
Large Language Models (LLMs) have demonstrated remarkable proficiency in various natural language generation (NLG) tasks. Previous studies suggest that LLMs' generation process inv…
cs.CL2024
Defending LLMs against Jailbreaking Attacks via Backtranslation
Yihan Wang, Zhouxing Shi, Andrew Bai +1
Although many large language models (LLMs) have been trained to refuse harmful requests, they are still vulnerable to jailbreaking attacks which rewrite the original prompt to conc…