32 citations · 97 across the 33 of their papers we have counts for
4 papers · 1 filter
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…
Prompt Space Optimizing Few-shot Reasoning Success with Large Language Models
Fobo Shi, Peijun Qing, Dong Yang +5
Prompt engineering is an essential technique for enhancing the abilities of large language models (LLMs) by providing explicit and specific instructions. It enables LLMs to excel i…
Emily: Developing An Emotion-affective Open-Domain Chatbot with Knowledge Graph-based Persona
Weixuan Wang, Xiaoling Cai, Chong Hsuan Huang +4
In this paper, we describe approaches for developing Emily, an emotion-affective open-domain chatbot. Emily can perceive a user's negative emotion state and offer supports by posit…
KELM: Knowledge Enhanced Pre-Trained Language Representations with Message Passing on Hierarchical Relational Graphs
Yinquan Lu, Haonan Lu, Guirong Fu +1
Incorporating factual knowledge into pre-trained language models (PLM) such as BERT is an emerging trend in recent NLP studies. However, most of the existing methods combine the ex…