6 citations · 9 across the 17 of their papers we have counts for
6 papers · 1 filter
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.…
Boosting Pointer Analysis With LLM-Enhanced Allocation Function Detection
Baijun Cheng, Kailong Wang, Ling Shi +5
Pointer analysis is foundational for many static analysis tasks, yet its effectiveness is often hindered by imprecise modeling of heap allocations, particularly in C/C++ programs w…
Enhancing Semantic Understanding in Pointer Analysis using Large Language Models
Baijun Cheng, Kailong Wang, Ling Shi +4
Pointer analysis has been studied for over four decades. However, existing frameworks continue to suffer from the propagation of incorrect facts. A major limitation stems from thei…
Circumventing Safety Alignment in Large Language Models Through Embedding Space Toxicity Attenuation
Zhibo Zhang, Yuxi Li, Kailong Wang +3
Large Language Models (LLMs) have achieved remarkable success across domains such as healthcare, education, and cybersecurity. However, this openness also introduces significant se…
Exposing the Ghost in the Transformer: Abnormal Detection for Large Language Models via Hidden State Forensics
Shide Zhou, Kailong Wang, Ling Shi +1
The widespread adoption of Large Language Models (LLMs) in critical applications has introduced severe reliability and security risks, as LLMs remain vulnerable to notorious threat…
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…