graph-of-thoughts VS ChainFury

We introduce Graph of Thoughts (GoT): a framework that advances prompting capabilities in large language models (LLMs) beyond those offered by paradigms such as Chain-ofThought or Tree of Thoughts (ToT). The key idea and primary advantage of GoT is the ability to model the information generated by an LLM as an arbitrary graph, where units of information (“LLM thoughts”) are vertices, and edges correspond to dependencies between these vertices. This approach enables combining arbitrary LLM thoughts into synergistic outcomes, distilling the essence of whole networks of thoughts, or enhancing thoughts using feedback loops. We illustrate that GoT offers advantages over state of the art on different tasks, for example increasing the quality of sorting by 62% over ToT, while simultaneously reducing costs by >31%. We ensure that GoT is extensible with new thought transformations and thus can be used to spearhead new prompting schemes. This work brings the LLM reasoning closer to human thinking or brain mechanisms such as recurrence, both of which form complex networks. Website & code: https://github.com/spcl/graph-of-thoughts

https://github.com/spcl/graph-of-thoughts Code for the paper too

Check out our repo at https://github.com/NimbleBoxAI/ChainFury and give us a star ⭐️ to show your support! Thanks!

Our team accomplished this in a short period, and we'd love for you to check out our ChainFury Repo at https://github.com/NimbleBoxAI/ChainFury for more info and show your support by giving us a star ⭐️ on GitHub.

I wanted to share our latest project, ChainFury , with you all. It's an open-source tool that lets you build chat applications using LLMs with just 4 clicks