1 citations · 1 across the 2 of their papers we have counts for
4 papers
A Survey on the Honesty of Large Language Models
Siheng Li, Cheng Yang, Taiqiang Wu +12
Honesty is a fundamental principle for aligning large language models (LLMs) with human values, requiring these models to recognize what they know and don't know and be able to fai…
InsCL: A Data-efficient Continual Learning Paradigm for Fine-tuning Large Language Models with Instructions
Yifan Wang, Yafei Liu, Chufan Shi +4
Instruction tuning effectively optimizes Large Language Models (LLMs) for downstream tasks. Due to the changing environment in real-life applications, LLMs necessitate continual ta…
Specialist or Generalist? Instruction Tuning for Specific NLP Tasks
Chufan Shi, Yixuan Su, Cheng Yang +2
The potential of large language models (LLMs) to simultaneously perform a wide range of natural language processing (NLP) tasks has been the subject of extensive research. Although…
Assisting Language Learners: Automated Trans-Lingual Definition Generation via Contrastive Prompt Learning
Hengyuan Zhang, Dawei Li, Yanran Li +3
The standard definition generation task requires to automatically produce mono-lingual definitions (e.g., English definitions for English words), but ignores that the generated def…