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20232026
most citedHuatuoGPT-II, One-stage Training for Medical Adaption of LLMs

25 citations · 93 across the 35 of their papers we have counts for

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Showing 2024Show all

23 papers · 1 filter

cs.CL2024

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…

cs.CL2024★ 6 cited

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…

cs.HC2024

BlenderLLM: Training Large Language Models for Computer-Aided Design with Self-improvement

Yuhao Du, Shunian Chen, Wenbo Zan +6

The application of Large Language Models (LLMs) in Computer-Aided Design (CAD) remains an underexplored area, despite their remarkable advancements in other domains. In this paper,…

cs.CL2024

Alignment at Pre-training! Towards Native Alignment for Arabic LLMs

Juhao Liang, Zhenyang Cai, Jianqing Zhu +9

The alignment of large language models (LLMs) is critical for developing effective and safe language models. Traditional approaches focus on aligning models during the instruction…

cs.CV2024★ 1 cited

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…

cs.CL2024★ 1 cited

Second Language (Arabic) Acquisition of LLMs via Progressive Vocabulary Expansion

Jianqing Zhu, Huang Huang, Zhihang Lin +18

This paper addresses the critical need for democratizing large language models (LLM) in the Arab world, a region that has seen slower progress in developing models comparable to st…

cs.CV2024

Both Text and Images Leaked! A Systematic Analysis of Data Contamination in Multimodal LLM

Dingjie Song, Sicheng Lai, Mingxuan Wang +3

The rapid advancement of multimodal large language models (MLLMs) has significantly enhanced performance across benchmarks. However, data contamination-unintentional memorization o…

cs.CV2024

VLFeedback: A Large-Scale AI Feedback Dataset for Large Vision-Language Models Alignment

Lei Li, Zhihui Xie, Mukai Li +7

As large vision-language models (LVLMs) evolve rapidly, the demand for high-quality and diverse data to align these models becomes increasingly crucial. However, the creation of su…

cs.CL2024★ 1 cited

Roadmap towards Superhuman Speech Understanding using Large Language Models

Fan Bu, Yuhao Zhang, Xidong Wang +3

The success of large language models (LLMs) has prompted efforts to integrate speech and audio data, aiming to create general foundation models capable of processing both textual a…

cs.CL2024★ 3 cited

Beyond Binary: Towards Fine-Grained LLM-Generated Text Detection via Role Recognition and Involvement Measurement

Zihao Cheng, Li Zhou, Feng Jiang +2

The rapid development of large language models (LLMs), like ChatGPT, has resulted in the widespread presence of LLM-generated content on social media platforms, raising concerns ab…

cs.CL2024★ 1 cited

Efficiently Democratizing Medical LLMs for 50 Languages via a Mixture of Language Family Experts

Guorui Zheng, Xidong Wang, Juhao Liang +3

Adapting medical Large Language Models to local languages can reduce barriers to accessing healthcare services, but data scarcity remains a significant challenge, particularly for…

cs.CL2024★ 2 cited

LongLLaVA: Scaling Multi-modal LLMs to 1000 Images Efficiently via a Hybrid Architecture

Xidong Wang, Dingjie Song, Shunian Chen +5

Expanding the long-context capabilities of Multi-modal Large Language Models~(MLLMs) is critical for advancing video understanding and high-resolution image analysis. Achieving thi…

cs.CL2024★ 1 cited

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…

cs.CL2024★ 3 cited

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…

cs.CL2024

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…

cs.CV2024★ 8 cited

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…

cs.CL2024★ 1 cited

MileBench: Benchmarking MLLMs in Long Context

Dingjie Song, Shunian Chen, Guiming Hardy Chen +3

Despite the advancements and impressive performance of Multimodal Large Language Models (MLLMs) on benchmarks, their effectiveness in real-world, long-context, and multi-image task…

cs.AI2024★ 1 cited

LLMs for Mathematical Modeling: Towards Bridging the Gap between Natural and Mathematical Languages

Xuhan Huang, Qingning Shen, Yan Hu +2

Large Language Models (LLMs) have demonstrated strong performance across various natural language processing tasks, yet their proficiency in mathematical reasoning remains a key ch…

cs.CL2024★ 1 cited

Smurfs: Multi-Agent System using Context-Efficient DFSDT for Tool Planning

Junzhi Chen, Juhao Liang, Benyou Wang

Teaching large language models (LLMs) to use tools for solving complex problems can grant them human-like reasoning abilities. ReAct and its variants are popular frameworks for too…

cs.CL2024★ 2 cited

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…

cs.CL2024★ 12 cited

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…

cs.CL2024★ 4 cited

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…

cs.CL2024★ 9 cited

Humans or LLMs as the Judge? A Study on Judgement Biases

Guiming Hardy Chen, Shunian Chen, Ziche Liu +2

Adopting human and large language models (LLM) as judges (a.k.a human- and LLM-as-a-judge) for evaluating the performance of LLMs has recently gained attention. Nonetheless, this a…