Large Language Model based Multi-Agents: A Survey of Progress and Challenges
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Updated
Apr 24, 2024
Large Language Model based Multi-Agents: A Survey of Progress and Challenges
Awesome LLM Self-Consistency: a curated list of Self-consistency in Large Language Models
The data and implementation for the experiments in the paper "Flows: Building Blocks of Reasoning and Collaborating AI".
LLMs represent numbers on a helix and manipulate that helix to do addition.
A framework for evaluating the effectiveness of chain-of-thought reasoning in language models.
Awesome Mixture of Experts (MoE): A Curated List of Mixture of Experts (MoE) and Mixture of Multimodal Experts (MoME)
Compare the intelligence of different AIs using randomly generated tasks.
Repo for exploring the (in)effectiveness of chain of thought in planning
Awesome-LLM-Planning
Latent-Explorer is the Python implementation of the framework proposed in the paper "Unveiling LLMs: The Evolution of Latent Representations in a Dynamic Knowledge Graph".
Can LLMs really 'Think'? This repo contains implementation of different types of memory for LLMs.
Willamette is an Ollama-Powered Model Runner
Personal coach based on LLMs
CoT Reasoning in Autoregressive Image Generation
Quantifying how close an AI is to AGI at any given time
Experiments relating to synthetic LLM user-agents and LLM facilitators in online discussions
A powerful Python library that combines the Google Books API with Cohere's LLM capabilities to create an intelligent book discovery and analysis system. This tool enables sophisticated book searches, AI-powered analysis, and smart recommendations.
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