18 citations · 29 across the 23 of their papers we have counts for
22 papers · 1 filter
AgentEHR: Advancing Autonomous Clinical Decision-Making via Retrospective Summarization
Yusheng Liao, Chuan Xuan, Yutong Cai +4
Large Language Models have demonstrated profound utility in the medical domain. However, their application to autonomous Electronic Health Records~(EHRs) navigation remains constra…
Mining Useful General Data for Low-Resource Domain Adaptation
Pingjie Wang, Hongcheng Liu, Yusheng Liao +5
Adapting large language models (LLMs) to low-resource domains remains challenging due to the scarcity of domain-specific data. While in-domain data is limited, there exists a vast…
When Seeing Is not Enough: Revealing the Limits of Active Reasoning in MLLMs
Hongcheng Liu, Pingjie Wang, Yuhao Wang +3
Multimodal large language models (MLLMs) have shown strong capabilities across a broad range of benchmarks. However, most existing evaluations focus on passive inference, where mod…
Hierarchical Indexing with Knowledge Enrichment for Multilingual Video Corpus Retrieval
Yu Wang, Tianhao Tan, Yifei Wang
Retrieving relevant instructional videos from multilingual medical archives is crucial for answering complex, multi-hop questions across language boundaries. However, existing syst…
Overthinking Reduction with Decoupled Rewards and Curriculum Data Scheduling
Shuyang Jiang, Yusheng Liao, Ya Zhang +2
While large reasoning models trained with critic-free reinforcement learning and verifiable rewards (RLVR) represent the state-of-the-art, their practical utility is hampered by ``…
HeteroRAG: A Heterogeneous Retrieval-Augmented Generation Framework for Medical Vision Language Tasks
Zhe Chen, Yusheng Liao, Zhiyuan Zhu +4
Medical large vision-language Models (Med-LVLMs) have shown promise in clinical applications but suffer from factual inaccuracies and unreliable outputs, posing risks in real-world…
DSVD: Dynamic Self-Verify Decoding for Faithful Generation in Large Language Models
YiQiu Guo, Yuchen Yang, Zhe Chen +5
The reliability of large language models remains a critical challenge, particularly due to their susceptibility to hallucinations and factual inaccuracies during text generation. E…
MedS: Towards Medical Slow Thinking with Self-Evolved Soft Dual-sided Process Supervision
Shuyang Jiang, Yusheng Liao, Zhe Chen +3
Medical language models face critical barriers to real-world clinical reasoning applications. However, mainstream efforts, which fall short in task coverage, lack fine-grained supe…
Towards Omni-RAG: Comprehensive Retrieval-Augmented Generation for Large Language Models in Medical Applications
Zhe Chen, Yusheng Liao, Shuyang Jiang +4
Large language models hold promise for addressing medical challenges, such as medical diagnosis reasoning, research knowledge acquisition, clinical decision-making, and consumer he…
Drawing the Line: Enhancing Trustworthiness of MLLMs Through the Power of Refusal
Yuhao Wang, Zhiyuan Zhu, Heyang Liu +4
Multimodal large language models (MLLMs) excel at multimodal perception and understanding, yet their tendency to generate hallucinated or inaccurate responses undermines their trus…
ReflecTool: Towards Reflection-Aware Tool-Augmented Clinical Agents
Yusheng Liao, Shuyang Jiang, Yanfeng Wang +1
Large Language Models (LLMs) have shown promising potential in the medical domain, assisting with tasks like clinical note generation and patient communication. However, current LL…
Med-PMC: Medical Personalized Multi-modal Consultation with a Proactive Ask-First-Observe-Next Paradigm
Hongcheng Liu, Yusheng Liao, Siqv Ou +4
The application of the Multi-modal Large Language Models (MLLMs) in medical clinical scenarios remains underexplored. Previous benchmarks only focus on the capacity of the MLLMs in…
Decoding Linguistic Representations of Human Brain
Yu Wang, Heyang Liu, Yuhao Wang +6
Language, as an information medium created by advanced organisms, has always been a concern of neuroscience regarding how it is represented in the brain. Decoding linguistic repres…
Towards an End-to-End Framework for Invasive Brain Signal Decoding with Large Language Models
Sheng Feng, Heyang Liu, Yu Wang +1
In this paper, we introduce a groundbreaking end-to-end (E2E) framework for decoding invasive brain signals, marking a significant advancement in the field of speech neuroprosthesi…
MedCare: Advancing Medical LLMs through Decoupling Clinical Alignment and Knowledge Aggregation
Yusheng Liao, Shuyang Jiang, Zhe Chen +2
Large language models (LLMs) have shown substantial progress in natural language understanding and generation, proving valuable especially in the medical field. Despite advancement…
TAIA: Large Language Models are Out-of-Distribution Data Learners
Shuyang Jiang, Yusheng Liao, Ya Zhang +2
Fine-tuning on task-specific question-answer pairs is a predominant method for enhancing the performance of instruction-tuned large language models (LLMs) on downstream tasks. Howe…
MING-MOE: Enhancing Medical Multi-Task Learning in Large Language Models with Sparse Mixture of Low-Rank Adapter Experts
Yusheng Liao, Shuyang Jiang, Yu Wang +1
Large language models like ChatGPT have shown substantial progress in natural language understanding and generation, proving valuable across various disciplines, including the medi…
MAV: A Multimodal, Multigenre, and Multipurpose Audio-Visual Academic Lecture Dataset
Zhe Chen, Heyang Liu, Wenyi Yu +6
Publishing open-source academic video recordings is an emergent and prevalent approach to sharing knowledge online. Such videos carry rich multimodal information including speech,…
Automatic Interactive Evaluation for Large Language Models with State Aware Patient Simulator
Yusheng Liao, Yutong Meng, Yuhao Wang +3
Large Language Models (LLMs) have demonstrated remarkable proficiency in human interactions, yet their application within the medical field remains insufficiently explored. Previou…
Leveraging Diverse Modeling Contexts with Collaborating Learning for Neural Machine Translation
Yusheng Liao, Yanfeng Wang, Yu Wang
Autoregressive (AR) and Non-autoregressive (NAR) models are two types of generative models for Neural Machine Translation (NMT). AR models predict tokens in a word-by-word manner a…
An Automatic Evaluation Framework for Multi-turn Medical Consultations Capabilities of Large Language Models
Yusheng Liao, Yutong Meng, Hongcheng Liu +2
Large language models (LLMs) have achieved significant success in interacting with human. However, recent studies have revealed that these models often suffer from hallucinations,…
SelfEvolve: A Code Evolution Framework via Large Language Models
Shuyang Jiang, Yuhao Wang, Yu Wang
Large language models (LLMs) have already revolutionized code generation, after being pretrained on publicly available code data. However, while various methods have been proposed…