Summary of Demystifying Chains, Trees, and Graphs Of Thoughts, by Maciej Besta et al.
Demystifying Chains, Trees, and Graphs of Thoughts
by Maciej Besta, Florim Memedi, Zhenyu Zhang, Robert Gerstenberger, Guangyuan Piao, Nils Blach, Piotr Nyczyk, Marcin Copik, Grzegorz Kwaśniewski, Jürgen Müller, Lukas Gianinazzi, Ales Kubicek, Hubert Niewiadomski, Aidan O’Mahony, Onur Mutlu, Torsten Hoefler
First submitted to arxiv on: 25 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers focus on improving large language models’ (LLMs) performance through innovative prompting techniques in natural language processing (NLP). They explore the paradigm of prompt engineering coupled with structures, such as Chain-of-Thought, Tree of Thoughts, or Graph of Thoughts, which significantly enhance LLMs’ capabilities to solve various tasks. The authors devise a general blueprint for effective and efficient LLM reasoning schemes by analyzing the prompt execution pipeline, building a taxonomy of structure-enhanced LLM reasoning schemes, and comparing existing prompting schemes using this taxonomy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, scientists are trying to make language models smarter by finding new ways to give them tasks. They’re looking at different structures or “maps” that help these models solve problems better. This research can lead to more creative writing, planning, and even solving math problems. The goal is to understand how these models work and improve their performance. |
Keywords
* Artificial intelligence * Natural language processing * Nlp * Prompt * Prompting
