Prompts

Apple’s Breakthrough in Model Language Efficiency: Uncovering Speculative Flow for Faster Inference

The advent of large language models (LLMs) heralded a new era of AI capabilities, enabling innovations in understanding and generating human language. Despite their remarkable efficiency, these models have a significant computational burden, particularly during the...

Meet VLM-CaR (Code as Reward): A Novel Machine Learning Framework Empowering Reinforcement Learning with Vision-Language Models

Researchers from Google DeepMind worked with Mila and McGill University to define appropriate reward functions to address the challenge of effectively training reinforcement learning (RL) agents. The reinforcement learning method uses a reward system to achieve...

Researchers from AWS AI Labs and USC Propose Accord: A Machine Learning Framework That Allows User-Customizable Reward Functions and Enables Decode-Time Alignment of LLMs

A critical challenge at the core of developments in large language models (LLMs) is ensuring that their outputs align with the standards and intentions of human morality. Despite their complexity, these models can generate content that may be technically accurate, but...

Researchers from Meta AI and UCSD present TOOLVERIFIER: A Generation and Self-Verification Method for Enhancing the Performance of Tool Calls for LLMs

The integration of external tools into language models (LM) marks a decisive advance in the creation of flexible digital assistants. This integration enhances the functionality of the models and pushes them closer to the vision of general purpose artificial...

Researchers from NVIDIA and University of Maryland Propose ODIN: A Hacking-Mitigating Reward Decomposition Technique in Reinforcement Learning from Human Feedback (RLHF)

The popular Artificial Intelligence (AI) based chatbot i.e. ChatGPT, which is built on top of GPT's transformer architecture, uses the Reinforcement Learning from Human Feedback (RLHF) technique. RLHF is an increasingly important method for harnessing the power of...

Google AI introduces an efficient machine learning method for scaling large transformer-based language models (LLMs) on infinitely large inputs

Apr 15, 2024 | Prompts

Memory is important to intelligence as it helps recall past experiences and apply them to current situations. However, due to the way their attention mechanism works, both conventional Transformer models and Transformer-based Large Language Models (LLMs) have...

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