Recent research highlights the effectiveness of pre-trained and frozen Large Language Models (LLMs) in mapping scene re-arrangement instructions to programs for robots. The study emphasizes the adaptability of LLMs to open-domain natural language and user-specific procedures. Prompt engineering is identified as a key method to enhance LLM performance in various tasks such as coding, planning, and robotics.
Natural Language Enhances LLM Performance in Programming, Planning, and Robotics #abstractions #Ada #AI #AItaskplanning #artificialintelligence #Codesynthesis #interpretablecodelibraries #languagebasedactionlibraries #LGA #LILO #llm #machinelearning https://t.co/0bCpgqVCJz https://t.co/VYSII8N3NP
Natural language boosts LLM performance in coding, planning and robotics #DisruptiveTech https://t.co/SwLY7pu9q0
Here are the basic ideas behind prompt engineering and how they can be applied to improve the performance of a large language model (LLM)... Interface of an LLM: One major reason that LLMs are so popular is because their text-to-text interface makes them incredibly simple to… https://t.co/su6FPBD4DF
how was llama-3-vision trained? thanks to the very similar embedding spaces between base LLMs and their instruction-tuned counterparts, prompt understanding abilities are maintained while feeding one model's embeddings to the other. @yeswondwerr and i decided to test this by… https://t.co/9QMDkr5xQC
Pre-trained and frozen LLMs can effectively map simple scene re-arrangement instructions to programs over a robot's visuomotor functions through appropriate few-shot example prompting. To parse open-domain natural language and adapt to a user's idiosyncratic procedures, not known… https://t.co/NeYIa38EZU