25 citations · 107 across the 14 of their papers we have counts for
14 papers
Incorporating Lexical and Syntactic Knowledge for Unsupervised Cross-Lingual Transfer
Jianyu Zheng, Fengfei Fan, Jianquan Li
Unsupervised cross-lingual transfer involves transferring knowledge between languages without explicit supervision. Although numerous studies have been conducted to improve perform…
Online Training of Large Language Models: Learn while chatting
Juhao Liang, Ziwei Wang, Zhuoheng Ma +4
Large Language Models(LLMs) have dramatically revolutionized the field of Natural Language Processing(NLP), offering remarkable capabilities that have garnered widespread usage. Ho…
ALLaVA: Harnessing GPT4V-Synthesized Data for Lite Vision-Language Models
Guiming Hardy Chen, Shunian Chen, Ruifei Zhang +7
Large vision-language models (LVLMs) have shown premise in a broad range of vision-language tasks with their strong reasoning and generalization capabilities. However, they require…
MLLM-Bench: Evaluating Multimodal LLMs with Per-sample Criteria
Wentao Ge, Shunian Chen, Guiming Hardy Chen +14
Multimodal large language models (MLLMs) have broadened the scope of AI applications. Existing automatic evaluation methodologies for MLLMs are mainly limited in evaluating queries…
HuatuoGPT-II, One-stage Training for Medical Adaption of LLMs
Junying Chen, Xidong Wang, Ke Ji +11
Adapting a language model into a specific domain, a.k.a `domain adaption', is a common practice when specialized knowledge, e.g. medicine, is not encapsulated in a general language…
AceGPT, Localizing Large Language Models in Arabic
Huang Huang, Fei Yu, Jianqing Zhu +17
This paper is devoted to the development of a localized Large Language Model (LLM) specifically for Arabic, a language imbued with unique cultural characteristics inadequately addr…
CMB: A Comprehensive Medical Benchmark in Chinese
Xidong Wang, Guiming Hardy Chen, Dingjie Song +8
Large Language Models (LLMs) provide a possibility to make a great breakthrough in medicine. The establishment of a standardized medical benchmark becomes a fundamental cornerstone…
HuatuoGPT, towards Taming Language Model to Be a Doctor
Hongbo Zhang, Junying Chen, Feng Jiang +10
In this paper, we present HuatuoGPT, a large language model (LLM) for medical consultation. The core recipe of HuatuoGPT is to leverage both \textit{distilled data from ChatGPT} an…
Huatuo-26M, a Large-scale Chinese Medical QA Dataset
Jianquan Li, Xidong Wang, Xiangbo Wu +6
In this paper, we release a largest ever medical Question Answering (QA) dataset with 26 million QA pairs. We benchmark many existing approaches in our dataset in terms of both ret…
Phoenix: Democratizing ChatGPT across Languages
Zhihong Chen, Feng Jiang, Junying Chen +11
This paper presents our efforts to democratize ChatGPT across language. We release a large language model "Phoenix", achieving competitive performance among open-source English and…
Effective Open Intent Classification with K-center Contrastive Learning and Adjustable Decision Boundary
Xiaokang Liu, Jianquan Li, Jingjing Mu +3
Open intent classification, which aims to correctly classify the known intents into their corresponding classes while identifying the new unknown (open) intents, is an essential bu…
Can Language Models Make Fun? A Case Study in Chinese Comical Crosstalk
Benyou Wang, Xiangbo Wu, Xiaokang Liu +3
Language is the principal tool for human communication, in which humor is one of the most attractive parts. Producing natural language like humans using computers, a.k.a, Natural L…
BERT-EMD: Many-to-Many Layer Mapping for BERT Compression with Earth Mover's Distance
Jianquan Li, Xiaokang Liu, Honghong Zhao +3
Pre-trained language models (e.g., BERT) have achieved significant success in various natural language processing (NLP) tasks. However, high storage and computational costs obstruc…
Empirical Evaluation of Multi-task Learning in Deep Neural Networks for Natural Language Processing
Jianquan Li, Xiaokang Liu, Wenpeng Yin +3
Multi-Task Learning (MTL) aims at boosting the overall performance of each individual task by leveraging useful information contained in multiple related tasks. It has shown great…