3 citations · 10 across the 8 of their papers we have counts for
11 papers
From Memory to Behavior: A Behavior-Aware Role-Playing Framework for Social Media Influencers
Ji-Lun Peng, Yi-Zhen Zhang, Chun-Nan Chou +1
Large language models have shown strong potential as role-playing agents for real individuals, yet faithful impersonating remains challenging. Existing in-context learning-based me…
OpenSage: Self-programming Agent Generation Engine
Hongwei Li, Zhun Wang, Qinrun Dai +11
Agent development kits (ADKs) provide effective platforms and tooling for constructing agents, and their designs are critical to the constructed agents' performance, especially the…
SWE-Spot: Building Small Repo-Experts with Repository-Centric Learning
Jinjun Peng, Magnus Saebo, Tianjun Zhong +6
The deployment of coding agents in privacy-sensitive and resource-constrained environments drives the demand for capable open-weight Small Language Models (SLMs). However, they suf…
Your Compiler is Backdooring Your Model: Understanding and Exploiting Compilation Inconsistency Vulnerabilities in Deep Learning Compilers
Simin Chen, Jinjun Peng, Yixin He +2
Deep learning (DL) compilers are core infrastructure in modern DL systems, offering flexibility and scalability beyond vendor-specific libraries. This work uncovers a fundamental v…
Proactive defense against LLM Jailbreak
Weiliang Zhao, Jinjun Peng, Daniel Ben-Levi +2
The proliferation of powerful large language models (LLMs) has necessitated robust safety alignment, yet these models remain vulnerable to evolving adversarial attacks, including m…
CodeSense: a Real-World Benchmark and Dataset for Code Semantic Reasoning
Monoshi Kumar Roy, Simin Chen, Benjamin Steenhoek +4
Understanding and reasoning about code semantics is essential for enhancing code LLMs' abilities to solve real-world software engineering (SE) tasks. Although several code reasonin…
CWEval: Outcome-driven Evaluation on Functionality and Security of LLM Code Generation
Jinjun Peng, Leyi Cui, Kele Huang +2
Large Language Models (LLMs) have significantly aided developers by generating or assisting in code writing, enhancing productivity across various tasks. While identifying incorrec…
A Survey of Useful LLM Evaluation
Ji-Lun Peng, Sijia Cheng, Egil Diau +4
LLMs have gotten attention across various research domains due to their exceptional performance on a wide range of complex tasks. Therefore, refined methods to evaluate the capabil…
SemCoder: Training Code Language Models with Comprehensive Semantics Reasoning
Yangruibo Ding, Jinjun Peng, Marcus J. Min +3
Code Large Language Models (Code LLMs) have excelled at tasks like code completion but often miss deeper semantics such as execution effects and dynamic states. This paper aims to…
Quarl: A Learning-Based Quantum Circuit Optimizer
Zikun Li, Jinjun Peng, Yixuan Mei +4
Optimizing quantum circuits is challenging due to the very large search space of functionally equivalent circuits and the necessity of applying transformations that temporarily dec…
NeuRI: Diversifying DNN Generation via Inductive Rule Inference
Jiawei Liu, Jinjun Peng, Yuyao Wang +1
Deep Learning (DL) is prevalently used in various industries to improve decision-making and automate processes, driven by the ever-evolving DL libraries and compilers. The correctn…