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pro vyhledávání: '"Tekin, Selim Furkan"'
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
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
Existing approaches to the crime prediction problem are unsuccessful in expressing the details since they assign the probability values to large regions. This paper introduces a new architecture with the graph convolutional networks (GCN) and multiva
Externí odkaz:
http://arxiv.org/abs/2111.14733
Numerical Weather Forecasting using Convolutional-LSTM with Attention and Context Matcher Mechanisms
Numerical weather forecasting using high-resolution physical models often requires extensive computational resources on supercomputers, which diminishes their wide usage in most real-life applications. As a remedy, applying deep learning methods has
Externí odkaz:
http://arxiv.org/abs/2102.00696
Akademický článek
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Publikováno v:
Signal, Image and Video Processing
Existing approaches to the crime prediction problem are unsuccessful in expressing the details since they assign the probability values to large regions. This paper introduces a new architecture with the graph convolutional networks (GCN) and multiva
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c1dedee711106069c023adf05459f67d
https://doi.org/10.21203/rs.3.rs-1508116/v1
https://doi.org/10.21203/rs.3.rs-1508116/v1