3 citations · 5 across the 6 of their papers we have counts for
6 papers · 1 filter
Taking the PULSE of Medical Reasoning: Step-Level Retrieval Timing for Knowledge-Grounded Inference
Danyang Zhao, Sendong Zhao, Yanrui Du +6
Reliable medical reasoning requires LLMs to dynamically access external biomedical evidence to improve factual trustworthiness and reasoning accuracy. However, indiscriminate retri…
S3-CoT: Self-Sampled Succinct Reasoning Enables Efficient Chain-of-Thought LLMs
Yanrui Du, Sendong Zhao, Yibo Gao +9
Large language models (LLMs) equipped with chain-of-thought (CoT) achieve strong performance and offer a window into LLM behavior. However, recent evidence suggests that improvemen…
Toward Secure Tuning: Mitigating Security Risks from Instruction Fine-Tuning
Yanrui Du, Sendong Zhao, Jiawei Cao +6
Instruction fine-tuning has emerged as a critical technique for customizing Large Language Models (LLMs) to specific applications. However, recent studies have highlighted signific…
CFSP: An Efficient Structured Pruning Framework for LLMs with Coarse-to-Fine Activation Information
Yuxin Wang, Minghua Ma, Zekun Wang +7
The colossal parameters and computational overhead of Large Language Models (LLMs) challenge their real-world applications. Network pruning, which targets unstructured or structure…
MoGU: A Framework for Enhancing Safety of Open-Sourced LLMs While Preserving Their Usability
Yanrui Du, Sendong Zhao, Danyang Zhao +6
Large Language Models (LLMs) are increasingly deployed in various applications. As their usage grows, concerns regarding their safety are rising, especially in maintaining harmless…
Analyzing the Inherent Response Tendency of LLMs: Real-World Instructions-Driven Jailbreak
Yanrui Du, Sendong Zhao, Ming Ma +2
Extensive work has been devoted to improving the safety mechanism of Large Language Models (LLMs). However, LLMs still tend to generate harmful responses when faced with malicious…