13 citations · 52 across the 19 of their papers we have counts for
8 papers · 1 filter
Instruction Alignment for Binary Code Representation Learning
Huaijin Wang, Shuai Wang
Binary code representation learning is a fundamental problem in software security and reverse engineering. Existing methods mainly learn function-level embeddings that capture coar…
KQFuzz: Knowledge-Guided Fuzzing for Quantum Libraries via Large Language Models
Fuyuan Xia, Qixin Zhang, Chenhao Ying +5
As quantum computing continually improves, ensuring the reliability and correctness of quantum libraries has become increasingly critical. To this end, many LLM-based fuzzing appro…
Digging Into the Internal: Causality-Based Analysis of LLM Function Calling
Zhenlan Ji, Daoyuan Wu, Wenxuan Wang +3
Function calling (FC) has emerged as a powerful technique for facilitating large language models (LLMs) to interact with external systems and perform structured tasks. However, the…
From Evaluation to Enhancement: Large Language Models for Zero-Knowledge Proof Code Generation
Zhantong Xue, Pingchuan Ma, Zhaoyu Wang +4
Zero-knowledge proofs (ZKPs) are increasingly deployed in domains such as privacy-preserving authentication, verifiable computation, and secure finance. However, authoring ZK progr…
Enabling Runtime Verification of Causal Discovery Algorithms with Automated Conditional Independence Reasoning (Extended Version)
Pingchuan Ma, Zhenlan Ji, Peisen Yao +2
Causal discovery is a powerful technique for identifying causal relationships among variables in data. It has been widely used in various applications in software engineering. Caus…
"Oops, Did I Just Say That?" Testing and Repairing Unethical Suggestions of Large Language Models with Suggest-Critique-Reflect Process
Pingchuan Ma, Zongjie Li, Ao Sun +1
As the popularity of large language models (LLMs) soars across various applications, ensuring their alignment with human values has become a paramount concern. In particular, given…
On Extracting Specialized Code Abilities from Large Language Models: A Feasibility Study
Zongjie Li, Chaozheng Wang, Pingchuan Ma +5
Recent advances in large language models (LLMs) significantly boost their usage in software engineering. However, training a well-performing LLM demands a substantial workforce for…
Unleashing the Power of Compiler Intermediate Representation to Enhance Neural Program Embeddings
Zongjie Li, Pingchuan Ma, Huaijin Wang +4
Neural program embeddings have demonstrated considerable promise in a range of program analysis tasks, including clone identification, program repair, code completion, and program…