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Large Language Models (LLMs) have shown impressive abilities in various tasks, but questions remain about their intelligence and reasoning capabilities. New methods like Chain-of-Abstraction (CoA) and Simulated Trial-and-Error (STE) are proposed to enhance LLMs' reasoning and tool usage. Researchers are exploring ways to improve LLMs' performance and collaboration through innovative techniques like Co-LLM and reinforcement learning.
Can LLMs plan and reason? That and more AI papers I read this week: - GaLore - KnowAgent - LLMs for Law - Design2Code - Claude 3 Technical Report - Can LLMs Reason and Plan? - Robust Evaluation of Reasoning I am most excited about the discussion around reasoning and planning…
Can LLMs Reason and Plan? There is a lot of debate about whether LLMs can reason and plan. These are important capabilities for unlocking complex applications with LLMs such as in the domains of robotics and autonomous agents. This position paper discusses the topic of… https://t.co/hIeGLi2VKO
Thanks @_akhaliq for sharing our work! Tool augmentation is essential for LLMs, but a critical aspect that has surprisingly been understudied is simply how accurately an LLM uses the tools being integrated. We propose Simulated Trial-and-Error (STE), a biologically-inspired… https://t.co/fD4vs9R9T4
