Weak-to-strong generalization
We present a new research direction for superalignment, together with promising initial results: can we leverage the generalization properties of deep learning to control strong models with weak supervisors?
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Tiny Titans Triumph: The Surprising Efficiency of Compact LLMs Exposed!
In the rapidly advancing field of natural language processing (NLP), the advent of large language models (LLMs) has significantly transformed. These models have shown remarkable success in understanding and generating human-like text across various tasks without specific training. However, the deployment of such models in real-world scenarios is often hindered by their substantial demand for…
Disrupting malicious uses of AI by state-affiliated threat actors
We terminated accounts associated with state-affiliated threat actors. Our findings show our models offer only limited, incremental capabilities for malicious cybersecurity tasks.
Parameter-Efficient Sparsity Crafting (PESC): A Novel AI Approach to Transition Dense Models to Sparse Models Using a Mixture-of-Experts (Moe) Architecture
The emergence of large language models (LLMs) like GPT, Claude, Gemini, LLaMA, Mistral, etc., has greatly accelerated recent advances in natural language processing (NLP). Instruction tweaking is a well-known approach to training LLMs. This method allows LLMs to improve their pre-trained representations to follow human instructions using large-scale, well-formatted instruction data. However, these tasks are…
Researchers from Stanford and OpenAI Introduce ‘Meta-Prompting’: An Effective Scaffolding Technique Designed to Enhance the Functionality of Language Models in a Task-Agnostic Manner
Language models (LMs), such as GPT-4, are at the forefront of natural language processing, offering capabilities that range from crafting complex prose to solving intricate computational problems. Despite their advanced functionalities, these models need fixing, sometimes yielding inaccurate or conflicting outputs. The challenge lies in enhancing their precision and versatility, particularly in complex, multi-faceted tasks….
This AI Paper Unveils Mixed-Precision Training for Fourier Neural Operators: Bridging Efficiency and Precision in High-Resolution PDE Solutions
Neural operators, specifically the Fourier Neural Operators (FNO), have revolutionized how researchers approach solving partial differential equations (PDEs), a cornerstone problem in science and engineering. These operators have shown exceptional promise in learning mappings between function spaces, pivotal for accurately simulating phenomena like climate modeling and fluid dynamics. Despite their potential, the substantial computational resources…
