3 citations · 12 across the 32 of their papers we have counts for
24 papers · 1 filter
MedUAG: Unified Understanding and Generation for Medical Multimodal Models
Zijie Meng, Yuncheng Zhang, Hualiang Wang +8
Recent Multimodal Large Language Models (MLLMs) are rapidly evolving into unified understanding and generation (UAG) frameworks. However, extending these unified paradigms to the m…
AgentSwing: Adaptive Parallel Context Management Routing for Long-Horizon Web Agents
Zhaopeng Feng, Liangcai Su, Zhen Zhang +16
As large language models (LLMs) evolve into autonomous agents for long-horizon information-seeking, managing finite context capacity has become a critical bottleneck. Existing cont…
LongWeave: A Long-Form Generation Benchmark Bridging Real-World Relevance and Verifiability
Zikai Xiao, Fei Huang, Jianhong Tu +7
Generating long, informative, and factual outputs remains a major challenge for Large Language Models (LLMs). Existing benchmarks for long-form generation typically assess real-wor…
DentalBench: Benchmarking and Advancing LLMs Capability for Bilingual Dentistry Understanding
Hengchuan Zhu, Yihuan Xu, Yichen Li +2
Recent advances in large language models (LLMs) and medical LLMs (Med-LLMs) have demonstrated strong performance on general medical benchmarks. However, their capabilities in speci…
Knowing or Guessing? Robust Medical Visual Question Answering via Joint Consistency and Contrastive Learning
Songtao Jiang, Yuxi Chen, Sibo Song +5
In high-stakes medical applications, consistent answering across diverse question phrasings is essential for reliable diagnosis. However, we reveal that current Medical Vision-Lang…
MedEthicsQA: A Comprehensive Question Answering Benchmark for Medical Ethics Evaluation of LLMs
Jianhui Wei, Zijie Meng, Zikai Xiao +5
While Medical Large Language Models (MedLLMs) have demonstrated remarkable potential in clinical tasks, their ethical safety remains insufficiently explored. This paper introduces…
Mitigating Posterior Salience Attenuation in Long-Context LLMs with Positional Contrastive Decoding
Zikai Xiao, Ziyang Wang, Wen Ma +5
While Large Language Models (LLMs) support long contexts, they struggle with performance degradation within the context window. Current solutions incur prohibitive training costs,…
Fast or Slow? Integrating Fast Intuition and Deliberate Thinking for Enhancing Visual Question Answering
Songtao Jiang, Chenyi Zhou, Yan Zhang +2
Multimodal large language models (MLLMs) still struggle with complex reasoning tasks in Visual Question Answering (VQA). While current methods have advanced by incorporating visual…
CP-Router: An Uncertainty-Aware Router Between LLM and LRM
Jiayuan Su, Fulin Lin, Zhaopeng Feng +7
Recent advances in Large Reasoning Models (LRMs) have significantly improved long-chain reasoning capabilities over Large Language Models (LLMs). However, LRMs often produce unnece…
MT: Scaling MLLM-based Text Image Machine Translation via Multi-Task Reinforcement Learning
Zhaopeng Feng, Yupu Liang, Shaosheng Cao +7
Text Image Machine Translation (TIMT)-the task of translating textual content embedded in images-is critical for applications in accessibility, cross-lingual information access, an…
OmniV-Med: Scaling Medical Vision-Language Model for Universal Visual Understanding
Songtao Jiang, Yuan Wang, Sibo Song +6
The practical deployment of medical vision-language models (Med-VLMs) necessitates seamless integration of textual data with diverse visual modalities, including 2D/3D images and v…
MT-R1-Zero: Advancing LLM-based Machine Translation via R1-Zero-like Reinforcement Learning
Zhaopeng Feng, Shaosheng Cao, Jiahan Ren +7
Large-scale reinforcement learning (RL) methods have proven highly effective in enhancing the reasoning abilities of large language models (LLMs), particularly for tasks with verif…
FairSteer: Inference Time Debiasing for LLMs with Dynamic Activation Steering
Yichen Li, Zhiting Fan, Ruizhe Chen +4
Large language models (LLMs) are prone to capturing biases from training corpus, leading to potential negative social impacts. Existing prompt-based debiasing methods exhibit insta…
MT-RewardTree: A Comprehensive Framework for Advancing LLM-Based Machine Translation via Reward Modeling
Zhaopeng Feng, Jiahan Ren, Jiayuan Su +3
Process reward models (PRMs) have shown success in complex reasoning tasks for large language models (LLMs). However, their application to machine translation (MT) remains underexp…
M-MAD: Multidimensional Multi-Agent Debate for Advanced Machine Translation Evaluation
Zhaopeng Feng, Jiayuan Su, Jiamei Zheng +5
Recent advancements in large language models (LLMs) have given rise to the LLM-as-a-judge paradigm, showcasing their potential to deliver human-like judgments. However, in the fiel…
EC-Guide: A Comprehensive E-Commerce Guide for Instruction Tuning and Quantization
Zhaopeng Feng, Zijie Meng, Zuozhu Liu
Large language models (LLMs) have attracted considerable attention in various fields for their cost-effective solutions to diverse challenges, especially with advancements in instr…
DynaThink: Fast or Slow? A Dynamic Decision-Making Framework for Large Language Models
Jiabao Pan, Yan Zhang, Chen Zhang +3
Large language models (LLMs) have demonstrated emergent capabilities across diverse reasoning tasks via popular Chains-of-Thought (COT) prompting. However, such a simple and fast C…
Ladder: A Model-Agnostic Framework Boosting LLM-based Machine Translation to the Next Level
Zhaopeng Feng, Ruizhe Chen, Yan Zhang +2
General-purpose Large Language Models (LLMs) like GPT-4 have achieved remarkable advancements in machine translation (MT) by leveraging extensive web content. On the other hand, tr…
Joint Visual and Text Prompting for Improved Object-Centric Perception with Multimodal Large Language Models
Songtao Jiang, Yan Zhang, Chenyi Zhou +4
Multimodal Large Language Models (MLLMs) such as GPT-4V and Gemini Pro face challenges in achieving human-level perception in Visual Question Answering (VQA), particularly in objec…
TEaR: Improving LLM-based Machine Translation with Systematic Self-Refinement
Zhaopeng Feng, Yan Zhang, Hao Li +7
Large Language Models (LLMs) have achieved impressive results in Machine Translation (MT). However, careful evaluations by human reveal that the translations produced by LLMs still…
DCR: Divide-and-Conquer Reasoning for Multi-choice Question Answering with LLMs
Zijie Meng, Yan Zhang, Zhaopeng Feng +1
Large language models (LLMs) have shown impressive performance in reasoning benchmarks with the emergence of Chain-of-Thought (CoT), particularly in multi-choice question (MCQ). Ho…
How Well Do Text Embedding Models Understand Syntax?
Yan Zhang, Zhaopeng Feng, Zhiyang Teng +2
Text embedding models have significantly contributed to advancements in natural language processing by adeptly capturing semantic properties of textual data. However, the ability o…
Self-Improving for Zero-Shot Named Entity Recognition with Large Language Models
Tingyu Xie, Qi Li, Yan Zhang +2
Exploring the application of powerful large language models (LLMs) on the named entity recognition (NER) task has drawn much attention recently. This work pushes the performance bo…
Empirical Study of Zero-Shot NER with ChatGPT
Tingyu Xie, Qi Li, Jian Zhang +3
Large language models (LLMs) exhibited powerful capability in various natural language processing tasks. This work focuses on exploring LLM performance on zero-shot information ext…