6 citations · 23 across the 17 of their papers we have counts for
8 papers · 1 filter
LLMs are Also Effective Embedding Models: An In-depth Overview
Chongyang Tao, Tao Shen, Shen Gao +6
Large language models (LLMs) have revolutionized natural language processing by achieving state-of-the-art performance across various tasks. Recently, their effectiveness as embedd…
Exploring LLM-based Data Annotation Strategies for Medical Dialogue Preference Alignment
Chengfeng Dou, Ying Zhang, Zhi Jin +4
This research examines the use of Reinforcement Learning from AI Feedback (RLAIF) techniques to improve healthcare dialogue models, with the aim of tackling the challenges of prefe…
EVIT: Event-Oriented Instruction Tuning for Event Reasoning
Zhengwei Tao, Xiancai Chen, Zhi Jin +3
Events refer to specific occurrences, incidents, or happenings that take place under a particular background. Event reasoning aims to infer events according to certain relations an…
MEEL: Multi-Modal Event Evolution Learning
Zhengwei Tao, Zhi Jin, Junqiang Huang +5
Multi-modal Event Reasoning (MMER) endeavors to endow machines with the ability to comprehend intricate event relations across diverse data modalities. MMER is fundamental and unde…
A Comprehensive Evaluation on Event Reasoning of Large Language Models
Zhengwei Tao, Zhi Jin, Yifan Zhang +7
Event reasoning is a fundamental ability that underlies many applications. It requires event schema knowledge to perform global reasoning and needs to deal with the diversity of th…
A Survey on Self-Evolution of Large Language Models
Zhengwei Tao, Ting-En Lin, Xiancai Chen +7
Large language models (LLMs) have significantly advanced in various fields and intelligent agent applications. However, current LLMs that learn from human or external model supervi…