4 citations · 6 across the 4 of their papers we have counts for
5 papers
SpecCloak: Probing Harmful Code Generation through Engineering Specifications
Wenbin Chen, Jiexin Wang, Guanting Wu +2
The code generation capabilities of large language models (LLMs) can equally be misused to implement harmful functionality, underscoring the need to understand their safety behavio…
Toward Secure Code Generation: Bridging Correctness and Security via Task-Adaptive Vulnerability Modeling and Execution-Based Benchmarking
Jiexin Wang, Xitong Luo, Liuwen Cao +9
Large language models (LLMs) are increasingly used for program synthesis, yet they often generate code that is functionally plausible but insecure. Progress in secure code generati…
Towards Effective Time-Aware Language Representation: Exploring Enhanced Temporal Understanding in Language Models
Jiexin Wang, Adam Jatowt, Yi Cai
In the evolving field of Natural Language Processing (NLP), understanding the temporal context of text is increasingly critical for applications requiring advanced temporal reasoni…
A Logical Pattern Memory Pre-trained Model for Entailment Tree Generation
Li Yuan, Yi Cai, Haopeng Ren +1
Generating coherent and credible explanations remains a significant challenge in the field of AI. In recent years, researchers have delved into the utilization of entailment trees…
Enhancing Large Language Models for Secure Code Generation: A Dataset-driven Study on Vulnerability Mitigation
Jiexin Wang, Liuwen Cao, Xitong Luo +4
Large language models (LLMs) have brought significant advancements to code generation, benefiting both novice and experienced developers. However, their training using unsanitized…