27 citations · 33 across the 5 of their papers we have counts for
5 papers
Beyond Correctness: Exposing LLM-generated Logical Flaws in Reasoning via Multi-step Automated Theorem Proving
Xinyi Zheng, Ningke Li, Xiaokun Luan +4
Large Language Models (LLMs) have demonstrated impressive reasoning capabilities, leading to their adoption in high-stakes domains such as healthcare, law, and scientific research.…
Large Language Models are overconfident and amplify human bias
Fengfei Sun, Ningke Li, Kailong Wang +1
Large language models (LLMs) are revolutionizing every aspect of society. They are increasingly used in problem-solving tasks to substitute human assessment and reasoning. LLMs are…
Detecting LLM Fact-conflicting Hallucinations Enhanced by Temporal-logic-based Reasoning
Ningke Li, Yahui Song, Kailong Wang +4
Large language models (LLMs) face the challenge of hallucinations -- outputs that seem coherent but are actually incorrect. A particularly damaging type is fact-conflicting halluci…
Large Language Models for Cyber Security: A Systematic Literature Review
Hanxiang Xu, Shenao Wang, Ningke Li +6
The rapid advancement of Large Language Models (LLMs) has opened up new opportunities for leveraging artificial intelligence in a variety of application domains, including cybersec…
Drowzee: Metamorphic Testing for Fact-Conflicting Hallucination Detection in Large Language Models
Ningke Li, Yuekang Li, Yi Liu +3
Large language models (LLMs) have transformed the landscape of language processing, yet struggle with significant challenges in terms of security, privacy, and the generation of se…