25 citations · 116 across the 24 of their papers we have counts for
28 papers
Legal-R1: Agentic Retrieval for Evidence-Based Legal Reasoning
Huaiwen Pang, Jiale Han, Junying Chen +2
Legal question answering requires answers that are not only plausible, but also grounded in legally valid and citable evidence. Standard retrieval-augmented generation often relies…
HuatuoGPT-3: RL-Only Domain Adaptation from Base Models
Junying Chen, Xinyuan Xie, Ziniu Li +7
Domain adaptation aims to turn a general-purpose large language model (LLM) into an expert for a target domain. While the dominant SFT+RL pipeline offers a convenient cold start, i…
From Large to Small: Transferring CUDA Optimization Expertise via Reasoning Graph
Junfeng Gong, Zhiyi Wei, Junying Chen +2
Despite significant evolution of CUDA programming and domain-specific libraries, effectively utilizing GPUs with massively parallel engines remains difficult. Large language models…
Enabling Doctor-Centric Medical AI with LLMs through Workflow-Aligned Tasks and Benchmarks
Wenya Xie, Qingying Xiao, Yu Zheng +8
The rise of large language models (LLMs) has transformed healthcare by offering clinical guidance, yet their direct deployment to patients poses safety risks due to limited domain…
WaveMind: Towards a Conversational EEG Foundation Model Aligned to Textual and Visual Modalities
Ziyi Zeng, Zhenyang Cai, Yixi Cai +8
Electroencephalography (EEG) interpretation using multimodal large language models (MLLMs) offers a novel approach for analyzing brain signals. However, the complex nature of brain…
ShizhenGPT: Towards Multimodal LLMs for Traditional Chinese Medicine
Junying Chen, Zhenyang Cai, Zhiheng Liu +10
Despite the success of large language models (LLMs) in various domains, their potential in Traditional Chinese Medicine (TCM) remains largely underexplored due to two critical barr…
TalkVid: A Large-Scale Diversified Dataset for Audio-Driven Talking Head Synthesis
Shunian Chen, Hejin Huang, Yexin Liu +10
Audio-driven talking head synthesis has achieved remarkable photorealism, yet state-of-the-art (SOTA) models exhibit a critical failure: they lack generalization to the full spectr…
MedGen: Unlocking Medical Video Generation by Scaling Granularly-annotated Medical Videos
Rongsheng Wang, Junying Chen, Ke Ji +4
Recent advances in video generation have shown remarkable progress in open-domain settings, yet medical video generation remains largely underexplored. Medical videos are critical…
ShareGPT-4o-Image: Aligning Multimodal Models with GPT-4o-Level Image Generation
Junying Chen, Zhenyang Cai, Pengcheng Chen +5
Recent advances in multimodal generative models have unlocked photorealistic, instruction-aligned image generation, yet leading systems like GPT-4o-Image remain proprietary and ina…
The First Few Tokens Are All You Need: An Efficient and Effective Unsupervised Prefix Fine-Tuning Method for Reasoning Models
Ke Ji, Jiahao Xu, Tian Liang +10
Improving the reasoning capabilities of large language models (LLMs) typically requires supervised fine-tuning with labeled data or computationally expensive sampling. We introduce…
Do LLMs Triage Like Clinicians? A Dynamic Study of Outpatient Referral
Xiaoxiao Liu, Qingying Xiao, Bingquan Zhang +8
Outpatient referral (OR) is a core clinical workflow that assigns patients to hospital departments under incomplete and evolving information, yet it is commonly simplified as a sta…
RAG-Instruct: Boosting LLMs with Diverse Retrieval-Augmented Instructions
Wanlong Liu, Junying Chen, Ke Ji +3
Retrieval-Augmented Generation (RAG) has emerged as a key paradigm for enhancing large language models (LLMs) by incorporating external knowledge. However, current RAG methods face…
HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs
Junying Chen, Zhenyang Cai, Ke Ji +5
The breakthrough of OpenAI o1 highlights the potential of enhancing reasoning to improve LLM. Yet, most research in reasoning has focused on mathematical tasks, leaving domains lik…
Exploring Compositional Generalization of Multimodal LLMs for Medical Imaging
Zhenyang Cai, Junying Chen, Rongsheng Wang +6
Medical imaging provides essential visual insights for diagnosis, and multimodal large language models (MLLMs) are increasingly utilized for its analysis due to their strong genera…
CoD, Towards an Interpretable Medical Agent using Chain of Diagnosis
Junying Chen, Chi Gui, Anningzhe Gao +4
The field of medical diagnosis has undergone a significant transformation with the advent of large language models (LLMs), yet the challenges of interpretability within these model…
LLMs for Doctors: Leveraging Medical LLMs to Assist Doctors, Not Replace Them
Wenya Xie, Qingying Xiao, Yu Zheng +7
The recent success of Large Language Models (LLMs) has had a significant impact on the healthcare field, providing patients with medical advice, diagnostic information, and more. H…
LLMs Could Autonomously Learn Without External Supervision
Ke Ji, Junying Chen, Anningzhe Gao +3
In the quest for super-human performance, Large Language Models (LLMs) have traditionally been tethered to human-annotated datasets and predefined training objectives-a process tha…
HuatuoGPT-Vision, Towards Injecting Medical Visual Knowledge into Multimodal LLMs at Scale
Junying Chen, Chi Gui, Ruyi Ouyang +10
The rapid development of multimodal large language models (MLLMs), such as GPT-4V, has led to significant advancements. However, these models still face challenges in medical multi…
Apollo: A Lightweight Multilingual Medical LLM towards Democratizing Medical AI to 6B People
Xidong Wang, Nuo Chen, Junyin Chen +9
Despite the vast repository of global medical knowledge predominantly being in English, local languages are crucial for delivering tailored healthcare services, particularly in are…
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…
Enhancing Open-Domain Table Question Answering via Syntax- and Structure-aware Dense Retrieval
Nengzheng Jin, Dongfang Li, Junying Chen +2
Open-domain table question answering aims to provide answers to a question by retrieving and extracting information from a large collection of tables. Existing studies of open-doma…
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…