There is great interest in using formal methods to guarantee the reliability of deep neural networks. However, these techniques may also be used to implant carefully selected input-output pairs. We present initial results on a novel technique for using SMT solvers to fine tune the weights of a ReLU neural network to guarantee outcomes on a finite set of particular examples. This procedure can be used to ensure performance on key examples, but it could also be used to insert difficult-to-find incorrect examples that trigger unexpected performance. We demonstrate this approach by fine tuning an MNIST network to incorrectly classify a particular image and discuss the potential for the approach to compromise reliability of freely-shared machine learning models.
Linear Separability of Activation Representations after Supervised Fine-Tuning on Incorrect Responses: A Study of Synthetic Dishonesty in Large Language Models
Algoverse AI Research；Medical Sciences Education Research Center, Mashhad University of Medical Sciences（马什哈德大学医学科学教育研究中心）；Student Research Committee, Department of Health Information Technology and Management, Medical Informatics, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences（谢赫·贝赫什提大学医学科学学院学生研究委员会，健康信息科技与管理系，医学信息学）
Offline Exploration-Aware Fine-Tuning for Long-Chain Mathematical Reasoning
NLP Lab, School of Computer Science and Engineering, Northeastern University, Shenyang, China（东北大学计算机科学与工程学院自然语言处理实验室，中国沈阳）；Pattern Recognition Center, WeChat AI, Tencent Inc, China（腾讯公司微信人工智能部门模式识别中心，中国）
A2D2: Fine-Tuning Any-Length Discrete Diffusion for Adaptive Decoding
Department of Computer and Information Science, University of Pennsylvania（宾夕法尼亚大学计算机与信息科学系）；Department of Bioengineering, University of Pennsylvania（宾夕法尼亚大学生物工程系）；School of Mathematics, Georgia Institute of Technology（佐治亚理工学院数学学院）
Why Fine-Tuning Encourages Hallucinations and How to Fix It
Hebrew University of Jerusalem（海法大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Technion – Israel Institute of Technology（技术学院 – 以色列理工学院）；University of Southern California（南加州大学）
FedMomentum: Preserving LoRA Training Momentum in Federated Fine-Tuning
Shanghai Jiao Tong University（上海交通大学）；Queen's University Belfast（女王大学贝尔法斯特分校）；Stevens Institute of Technology（史蒂文斯理工学院）；Purdue University（普渡大学）；Rice University（里奇大学）
The University of Hong Kong（香港大学）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；The Simons Institute for the Theory of Computing（西蒙斯理论计算研究所）；University of California, Berkeley（加州大学伯克利分校）；Sun Yat-sen University（中山大学）
Domain-Adaptive Pre-Training for Arabic Aspect-Based Sentiment Analysis: A Comparative Study of Domain Adaptation and Fine-Tuning Strategies
Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziza University, Jeddah, Saudi Arabia（计算机科学系，计算与信息技术学院，国王阿卜杜勒阿齐兹大学，沙特阿拉伯吉达）
