1 citations · 1 across the 7 of their papers we have counts for
7 papers · 1 filter
Controllable Affective Generation via Latent Vector Steering
Xixian Yong, Siyuan Chang, Yingying Zhang +2
Large Language Models (LLMs) often produce emotionally flattened responses after alignment, limiting their effectiveness in affect-sensitive applications. In this paper, we propose…
ECG-R1: Protocol-Guided and Modality-Agnostic MLLM for Reliable ECG Interpretation
Jiarui Jin, Haoyu Wang, Xingliang Wu +9
Electrocardiography (ECG) serves as an indispensable diagnostic tool in clinical practice, yet existing multimodal large language models (MLLMs) remain unreliable for ECG interpret…
From Retrieval to Generation: Unifying External and Parametric Knowledge for Medical Question Answering
Lei Li, Xiao Zhou, Yingying Zhang +1
Medical question answering (QA) requires extensive access to domain-specific knowledge. A promising direction is to enhance large language models (LLMs) with external knowledge ret…
Inflated Excellence or True Performance? Rethinking Medical Diagnostic Benchmarks with Dynamic Evaluation
Xiangxu Zhang, Lei Li, Yanyun Zhou +3
Medical diagnostics is a high-stakes and complex domain that is critical to patient care. However, current evaluations of large language models (LLMs) remain limited in capturing k…
Copy-Paste to Mitigate Large Language Model Hallucinations
Yongchao Long, Xian Wu, Yingying Zhang +3
While Retrieval-Augmented Generation (RAG) enables large language models (LLMs) to generate contextually grounded responses, contextual faithfulness remains challenging as LLMs may…
Think or Not? Exploring Thinking Efficiency in Large Reasoning Models via an Information-Theoretic Lens
Xixian Yong, Xiao Zhou, Yingying Zhang +3
The recent rise of Large Reasoning Models (LRMs) has significantly improved multi-step reasoning performance, but often at the cost of generating excessively long reasoning chains.…
Sliding Window Attention Training for Efficient Large Language Models
Zichuan Fu, Wentao Song, Yejing Wang +7
Recent advances in transformer-based Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks. However, their quadratic computational complexity…