Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Hu, Sihao"'
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
Autor:
Wu, Sifan, Chen, Haipeng, Yin, Yifang, Hu, Sihao, Feng, Runyang, Jiao, Yingying, Yang, Ziqi, Liu, Zhenguang
Human pose estimation in videos has long been a compelling yet challenging task within the realm of computer vision. Nevertheless, this task remains difficult because of the complex video scenes, such as video defocus and self-occlusion. Recent metho
Externí odkaz:
http://arxiv.org/abs/2408.02285
Face recognition (FR) can be abused for privacy intrusion. Governments, private companies, or even individual attackers can collect facial images by web scraping to build an FR system identifying human faces without their consent. This paper introduc
Externí odkaz:
http://arxiv.org/abs/2407.13975
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
We introduce PokeLLMon, the first LLM-embodied agent that achieves human-parity performance in tactical battle games, as demonstrated in Pokemon battles. The design of PokeLLMon incorporates three key strategies: (i) In-context reinforcement learning
Externí odkaz:
http://arxiv.org/abs/2402.01118
The new paradigm of finetuning-as-a-service introduces a new attack surface for Large Language Models (LLMs): a few harmful data uploaded by users can easily trick the finetuning to produce an alignment-broken model. We conduct an empirical analysis
Externí odkaz:
http://arxiv.org/abs/2402.01109