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LLM Powered Autonomous Agents

Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.
Agent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components: ....

Shinn Labash , Toolformer Schick , Prompt Engineering , Planning Domain Definition Language , World Env , Alfworld Env , Algorithm Distillation , Term Memory , Working Memory , Inner Product Search , Locality Sensitive Hashing , Approximate Nearest Neighbors Oh Yeah , Hierarchical Navigable Small World , Scalable Nearest Neighbors , Tool Augmented Language Models , Available Task List , Chat History , Candidate Models , User Input , Task Planning , Model Selection , Model Assignment , Task Execution , Search Engine , Dliberate Problem Solving , Large Language Models ,

Prompt Engineering

Prompt Engineering, also known as In-Context Prompting, refers to methods for how to communicate with LLM to steer its behavior for desired outcomes without updating the model weights. It is an empirical science and the effect of prompt engineering methods can vary a lot among models, thus requiring heavy experimentation and heuristics.
This post only focuses on prompt engineering for autoregressive language models, so nothing with Cloze tests, image generation or multimodality models. ....

Monte Carlo , Wikipedia Apis , Autoprompt Shin , Li Liang , Toolformer Schick , Program Of Thoughts , Prompt Engineering , In Context Prompting , Reinforcement Learning , Human Feedback , Self Taught Reasoner , Interleaving Retrieval Cot , Neural Text , Automatic Prompt Engineer , Open Domain Question Answering , Google Search , Tool Augmented Language Models , Before Use , Improving Few Shot Performance , Language Models , Makes Good In Context Examples , Ordered Prompts , Find Them , Overcoming Few Shot Prompt Order Sensitivity , Context Instruction Learning , Consistency Improves Chain ,