40 citations · 50 across the 13 of their papers we have counts for
13 papers
When Rules Learn: A Self-Evolving Agent for Legal Case Retrieval
Mingxu Tao, Jiawei Hu, Xian Zhou +5
Legal case retrieval remains challenging due to the complexity of legal language and the need for precise lexical alignment between queries and relevant cases. Although dense retri…
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…
ALOHA: Empowering Multilingual Agent for University Orientation with Hierarchical Retrieval
Mingxu Tao, Bowen Tang, Mingxuan Ma +5
The rise of Large Language Models~(LLMs) revolutionizes information retrieval, allowing users to obtain required answers through complex instructions within conversations. However,…
MiLiC-Eval: Benchmarking Multilingual LLMs for China's Minority Languages
Chen Zhang, Mingxu Tao, Zhiyuan Liao +1
Large language models (LLMs) excel in high-resource languages but struggle with low-resource languages (LRLs), particularly those spoken by minority communities in China, such as T…
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…
Can Perplexity Reflect Large Language Model's Ability in Long Text Understanding?
Yutong Hu, Quzhe Huang, Mingxu Tao +2
Recent studies have shown that Large Language Models (LLMs) have the potential to process extremely long text. Many works only evaluate LLMs' long-text processing ability on the la…
Harder Tasks Need More Experts: Dynamic Routing in MoE Models
Quzhe Huang, Zhenwei An, Nan Zhuang +7
In this paper, we introduce a novel dynamic expert selection framework for Mixture of Experts (MoE) models, aiming to enhance computational efficiency and model performance by adju…
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…
MC: Towards Transparent and Culturally-Aware NLP for Minority Languages in China
Chen Zhang, Mingxu Tao, Quzhe Huang +3
Current large language models demonstrate deficiencies in understanding low-resource languages, particularly the minority languages in China. This limitation stems from the scarcit…
A Frustratingly Easy Improvement for Position Embeddings via Random Padding
Mingxu Tao, Yansong Feng, Dongyan Zhao
Position embeddings, encoding the positional relationships among tokens in text sequences, make great contributions to modeling local context features in Transformer-based pre-trai…
Lawyer LLaMA Technical Report
Quzhe Huang, Mingxu Tao, Chen Zhang +5
Large Language Models (LLMs), like LLaMA, have exhibited remarkable performance across various tasks. Nevertheless, when deployed to specific domains such as law or medicine, the m…
Can BERT Refrain from Forgetting on Sequential Tasks? A Probing Study
Mingxu Tao, Yansong Feng, Dongyan Zhao
Large pre-trained language models help to achieve state of the art on a variety of natural language processing (NLP) tasks, nevertheless, they still suffer from forgetting when inc…