Graph of Thoughts: Solving Elaborate Problems with Large Language Models


  • Maciej Besta ETH Zurich
  • Nils Blach ETH Zurich
  • Ales Kubicek ETH Zurich
  • Robert Gerstenberger ETH Zurich
  • Michal Podstawski Warsaw University of Technology
  • Lukas Gianinazzi ETH Zurich
  • Joanna Gajda Cledar
  • Tomasz Lehmann Cledar
  • Hubert Niewiadomski Cledar
  • Piotr Nyczyk Cledar
  • Torsten Hoefler ETH Zurich



NLP: Conversational AI/Dialog Systems, NLP: (Large) Language Models, NLP: Question Answering, NLP: Summarization


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-of-Thought 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




How to Cite

Besta, M., Blach, N., Kubicek, A., Gerstenberger, R., Podstawski, M., Gianinazzi, L., Gajda, J., Lehmann, T., Niewiadomski, H., Nyczyk, P., & Hoefler, T. (2024). Graph of Thoughts: Solving Elaborate Problems with Large Language Models. Proceedings of the AAAI Conference on Artificial Intelligence, 38(16), 17682-17690.



AAAI Technical Track on Natural Language Processing I