From the 1 of 5 linked papers with an AI index.
5 papers
Code-MUE: Measuring Code LLMs' Uncertainty through Execution-based Semantic Interaction Graphs
Xiaoning Ren, Yinxing Xue, Lei Ma +1
The paper presents Code-MUE, a black‑box method that measures the uncertainty of code‑generating large language models by building execution‑based semantic interaction graphs and c…
Foundation Models for Autonomous Driving System: An Initial Roadmap
Xiongfei Wu, Mingfei Cheng, Xiaoning Ren +8
Recent advances in foundation models (FMs), including large language models (LLMs), vision-language models (VLMs), and world models, have opened new opportunities for autonomous dr…
Demystifying the Evolution of Neural Networks with BOM Analysis: Insights from a Large-Scale Study of 55,997 GitHub Repositories
Xiaoning Ren, Yuhang Ye, Xiongfei Wu +2
Neural networks have become integral to many fields due to their exceptional performance. The open-source community has witnessed a rapid influx of neural network (NN) repositories…
Improving Code Understanding in Large Language Models through Concept-Aware Consistency Learning
Xiaoning Ren, Qiang Hu, Wei Ma +6
Large language models (LLMs) have recently shown impressive results on diverse code-related tasks, benefiting from large-scale training and instruction tuning. However, studies rev…
Self and Cross-Model Distillation for LLMs: Effective Methods for Refusal Pattern Alignment
Jie Li, Yi Liu, Chongyang Liu +4
Large Language Models (LLMs) like OpenAI's GPT series, Anthropic's Claude, and Meta's LLaMa have shown remarkable capabilities in text generation. However, their susceptibility to…