5 papers
SALP-CG: Standard-Aligned LLM Pipeline for Classifying and Grading Large Volumes of Online Conversational Health Data
Yiwei Yan, Hao Li, Hua He +3
Online medical consultations generate large volumes of conversational health data that often embed protected health information, requiring robust methods to classify data categorie…
Unveiling and Causalizing CoT: A Causal Pespective
Jiarun Fu, Lizhong Ding, Hao Li +3
Although Chain-of-Thought (CoT) has achieved remarkable success in enhancing the reasoning ability of large language models (LLMs), the mechanism of CoT remains a ``black box''. Ev…
HealthQ: Unveiling Questioning Capabilities of LLM Chains in Healthcare Conversations
Ziyu Wang, Hao Li, Di Huang +3
Effective patient care in digital healthcare requires large language models (LLMs) that not only answer questions but also actively gather critical information through well-crafted…
RuozhiBench: Evaluating LLMs with Logical Fallacies and Misleading Premises
Zenan Zhai, Hao Li, Xudong Han +4
Recent advances in large language models (LLMs) have shown that they can answer questions requiring complex reasoning. However, their ability to identify and respond to text contai…
Skewed Memorization in Large Language Models: Quantification and Decomposition
Hao Li, Di Huang, Ziyu Wang +1
Memorization in Large Language Models (LLMs) poses privacy and security risks, as models may unintentionally reproduce sensitive or copyrighted data. Existing analyses focus on ave…