3 citations · 4 across the 5 of their papers we have counts for
5 papers
EpiCoDe: Boosting Model Performance Beyond Training with Extrapolation and Contrastive Decoding
Mingxu Tao, Jie Hu, Mingchuan Yang +3
The remarkable performance of Large language models (LLMs) relies heavily on the availability of abundant high-quality training data. However, the high cost of acquiring annotated…
Unlocking the Potential of Model Merging for Low-Resource Languages
Mingxu Tao, Chen Zhang, Quzhe Huang +4
Adapting large language models (LLMs) to new languages typically involves continual pre-training (CT) followed by supervised fine-tuning (SFT). However, this CT-then-SFT approach s…
Probing Multimodal Large Language Models for Global and Local Semantic Representations
Mingxu Tao, Quzhe Huang, Kun Xu +3
The advancement of Multimodal Large Language Models (MLLMs) has greatly accelerated the development of applications in understanding integrated texts and images. Recent works lever…
Chain-of-Discussion: A Multi-Model Framework for Complex Evidence-Based Question Answering
Mingxu Tao, Dongyan Zhao, Yansong Feng
Open-ended question answering requires models to find appropriate evidence to form wellreasoned, comprehensive and helpful answers. In practical applications, models also need to e…
Domain Adaptation for Semantic Parsing
Zechang Li, Yuxuan Lai, Yansong Feng +1
Recently, semantic parsing has attracted much attention in the community. Although many neural modeling efforts have greatly improved the performance, it still suffers from the dat…