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[PDF][PDF] How johnny can persuade llms to jailbreak them: Rethinking persuasion to challenge ai safety by humanizing llms
Most traditional AI safety research views models as machines and centers on algorithm-
focused attacks developed by security experts. As large language models (LLMs) become …
focused attacks developed by security experts. As large language models (LLMs) become …
Olmo: Accelerating the science of language models
D Groeneveld, I Beltagy, P Walsh, A Bhagia… - ar** the increasing use of LLMs in scientific papers
Scientific publishing lays the foundation of science by disseminating research findings,
fostering collaboration, encouraging reproducibility, and ensuring that scientific knowledge …
fostering collaboration, encouraging reproducibility, and ensuring that scientific knowledge …
Bigcodebench: Benchmarking code generation with diverse function calls and complex instructions
Task automation has been greatly empowered by the recent advances in Large Language
Models (LLMs) via Python code, where the tasks ranging from software engineering …
Models (LLMs) via Python code, where the tasks ranging from software engineering …
An archival perspective on pretraining data
Alongside an explosion in research and development related to large language models,
there has been a concomitant rise in the creation of pretraining datasets—massive …
there has been a concomitant rise in the creation of pretraining datasets—massive …
The responsible foundation model development cheatsheet: A review of tools & resources
Foundation model development attracts a rapidly expanding body of contributors, scientists,
and applications. To help shape responsible development practices, we introduce the …
and applications. To help shape responsible development practices, we introduce the …
No" zero-shot" without exponential data: Pretraining concept frequency determines multimodal model performance
Web-crawled pretraining datasets underlie the impressive" zero-shot" evaluation
performance of multimodal models, such as CLIP for classification and Stable-Diffusion for …
performance of multimodal models, such as CLIP for classification and Stable-Diffusion for …
The bias amplification paradox in text-to-image generation
Bias amplification is a phenomenon in which models exacerbate biases or stereotypes
present in the training data. In this paper, we study bias amplification in the text-to-image …
present in the training data. In this paper, we study bias amplification in the text-to-image …