2 citations · 4 across the 16 of their papers we have counts for
5 papers · 2 filters
Unveiling In-Context Learning: A Coordinate System to Understand Its Working Mechanism
Anhao Zhao, Fanghua Ye, Jinlan Fu +1
Large language models (LLMs) exhibit remarkable in-context learning (ICL) capabilities. However, the underlying working mechanism of ICL remains poorly understood. Recent research…
Chain of Thought Explanation for Dialogue State Tracking
Lin Xu, Ningxin Peng, Daquan Zhou +2
Dialogue state tracking (DST) aims to record user queries and goals during a conversational interaction achieved by maintaining a predefined set of slots and their corresponding va…
CET2: Modelling Topic Transitions for Coherent and Engaging Knowledge-Grounded Conversations
Lin Xu, Qixian Zhou, Jinlan Fu +1
Knowledge-grounded dialogue systems aim to generate coherent and engaging responses based on the dialogue contexts and selected external knowledge. Previous knowledge selection met…
Hint-before-Solving Prompting: Guiding LLMs to Effectively Utilize Encoded Knowledge
Jinlan Fu, Shenzhen Huangfu, Hang Yan +2
Large Language Models (LLMs) have recently showcased remarkable generalizability in various domains. Despite their extensive knowledge, LLMs still face challenges in efficiently ut…
LLM can Achieve Self-Regulation via Hyperparameter Aware Generation
Siyin Wang, Shimin Li, Tianxiang Sun +6
In the realm of Large Language Models (LLMs), users commonly employ diverse decoding strategies and adjust hyperparameters to control the generated text. However, a critical questi…