Zobrazeno 1 - 10
of 202
pro vyhledávání: '"İLHAN, FATİH"'
Alignment of pretrained LLMs using instruction-based datasets is critical for creating fine-tuned models that reflect human preference. A growing number of alignment-based fine-tuning algorithms and benchmarks emerged recently, fueling the efforts on
Externí odkaz:
http://arxiv.org/abs/2411.17792
Combining large language models during training or at inference time has shown substantial performance gain over component LLMs. This paper presents LLM-TOPLA, a diversity-optimized LLM ensemble method with three unique properties: (i) We introduce t
Externí odkaz:
http://arxiv.org/abs/2410.03953
Recent research demonstrates that the nascent fine-tuning-as-a-service business model exposes serious safety concerns -- fine-tuning over a few harmful data uploaded by the users can compromise the safety alignment of the model. The attack, known as
Externí odkaz:
http://arxiv.org/abs/2409.18169
Booster: Tackling Harmful Fine-tuning for Large Language Models via Attenuating Harmful Perturbation
Harmful fine-tuning issue \citep{qi2023fine} poses serious safety concerns for Large language models' fine-tuning-as-a-service. While existing defenses \citep{huang2024vaccine,rosati2024representation} have been proposed to mitigate the issue, their
Externí odkaz:
http://arxiv.org/abs/2409.01586
Recent studies show that Large Language Models (LLMs) with safety alignment can be jail-broken by fine-tuning on a dataset mixed with harmful data. First time in the literature, we show that the jail-broken effect can be mitigated by separating state
Externí odkaz:
http://arxiv.org/abs/2405.18641
Autor:
Tekin, Selim Furkan, Ilhan, Fatih, Huang, Tiansheng, Hu, Sihao, Chow, Ka-Ho, Loper, Margaret L., Liu, Ling
This paper presents FusionShot, a focal diversity optimized few-shot ensemble learning approach for boosting the robustness and generalization performance of pre-trained few-shot models. The paper makes three original contributions. First, we explore
Externí odkaz:
http://arxiv.org/abs/2404.04434
Autor:
Hu, Sihao, Huang, Tiansheng, Ilhan, Fatih, Tekin, Selim, Liu, Gaowen, Kompella, Ramana, Liu, Ling
The development of game agents holds a critical role in advancing towards Artificial General Intelligence (AGI). The progress of LLMs and their multimodal counterparts (MLLMs) offers an unprecedented opportunity to evolve and empower game agents with
Externí odkaz:
http://arxiv.org/abs/2404.02039
Publikováno v:
IEEE International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications 2023
This paper provides a systematic analysis of the opportunities, challenges, and potential solutions of harnessing Large Language Models (LLMs) such as GPT-4 to dig out vulnerabilities within smart contracts based on our ongoing research. For the task
Externí odkaz:
http://arxiv.org/abs/2310.01152
Federated Learning (FL) has been gaining popularity as a collaborative learning framework to train deep learning-based object detection models over a distributed population of clients. Despite its advantages, FL is vulnerable to model hijacking. The
Externí odkaz:
http://arxiv.org/abs/2303.11511
Autor:
Ilhan, Fatih, Chow, Ka-Ho, Hu, Sihao, Huang, Tiansheng, Tekin, Selim, Wei, Wenqi, Wu, Yanzhao, Lee, Myungjin, Kompella, Ramana, Latapie, Hugo, Liu, Gaowen, Liu, Ling
Well-trained deep neural networks (DNNs) treat all test samples equally during prediction. Adaptive DNN inference with early exiting leverages the observation that some test examples can be easier to predict than others. This paper presents EENet, a
Externí odkaz:
http://arxiv.org/abs/2301.07099