2 citations · 2 across the 2 of their papers we have counts for
4 papers
Entropy-KL Divergence-based Token Masking: A Novel Approach for Selective Fine-tuning of Large Language Models
Qi Liu, Mingdi Sun, Yongyi He +5
Supervised fine-tuning (SFT) followed by reinforcement learning (RL) has become a standard post-training paradigm for large language models. This paradigm provides a cold-start for…
UniMEL: A Unified Framework for Multimodal Entity Linking with Large Language Models
Liu Qi, He Yongyi, Lian Defu +4
Multimodal Entity Linking (MEL) is a crucial task that aims at linking ambiguous mentions within multimodal contexts to the referent entities in a multimodal knowledge base, such a…
In-Context Former: Lightning-fast Compressing Context for Large Language Model
Xiangfeng Wang, Zaiyi Chen, Zheyong Xie +3
With the rising popularity of Transformer-based large language models (LLMs), reducing their high inference costs has become a significant research focus. One effective approach is…
UniDM: A Unified Framework for Data Manipulation with Large Language Models
Yichen Qian, Yongyi He, Rong Zhu +8
Designing effective data manipulation methods is a long standing problem in data lakes. Traditional methods, which rely on rules or machine learning models, require extensive human…