3 citations · 14 across the 35 of their papers we have counts for
16 papers · 1 filter
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…
Reconstruct the Pruned Model without Any Retraining
Pingjie Wang, Ziqing Fan, Shengchao Hu +3
Structured pruning is a promising hardware-friendly compression technique for large language models (LLMs), which is expected to be retraining-free to avoid the enormous retraining…
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…
Post-decoder Biasing for End-to-End Speech Recognition of Multi-turn Medical Interview
Heyang Liu, Yu Wang, Yanfeng Wang
End-to-end (E2E) approach is gradually replacing hybrid models for automatic speech recognition (ASR) tasks. However, the optimization of E2E models lacks an intuitive method for h…
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…
M2K-VDG: Model-Adaptive Multimodal Knowledge Anchor Enhanced Video-grounded Dialogue Generation
Hongcheng Liu, Pingjie Wang, Yu Wang +1
Video-grounded dialogue generation (VDG) requires the system to generate a fluent and accurate answer based on multimodal knowledge. However, the difficulty in multimodal knowledge…
DictLLM: Harnessing Key-Value Data Structures with Large Language Models for Enhanced Medical Diagnostics
YiQiu Guo, Yuchen Yang, Ya Zhang +2
Structured data offers a sophisticated mechanism for the organization of information. Existing methodologies for the text-serialization of structured data in the context of large l…
MM-SAP: A Comprehensive Benchmark for Assessing Self-Awareness of Multimodal Large Language Models in Perception
Yuhao Wang, Yusheng Liao, Heyang Liu +3
Recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated exceptional capabilities in visual perception and understanding. However, these models also suffer…