Demystifying Chains, Trees, and Graphs of Thoughts
arxiv(2024)
摘要
The field of natural language processing (NLP) has witnessed significant
progress in recent years, with a notable focus on improving large language
models' (LLM) performance through innovative prompting techniques. Among these,
prompt engineering coupled with structures has emerged as a promising paradigm,
with designs such as Chain-of-Thought, Tree of Thoughts, or Graph of Thoughts,
in which the overall LLM reasoning is guided by a structure such as a graph. As
illustrated with numerous examples, this paradigm significantly enhances the
LLM's capability to solve numerous tasks, ranging from logical or mathematical
reasoning to planning or creative writing. To facilitate the understanding of
this growing field and pave the way for future developments, we devise a
general blueprint for effective and efficient LLM reasoning schemes. For this,
we conduct an in-depth analysis of the prompt execution pipeline, clarifying
and clearly defining different concepts. We then build the first taxonomy of
structure-enhanced LLM reasoning schemes. We focus on identifying fundamental
classes of harnessed structures, and we analyze the representations of these
structures, algorithms executed with these structures, and many others. We
refer to these structures as reasoning topologies, because their representation
becomes to a degree spatial, as they are contained within the LLM context. Our
study compares existing prompting schemes using the proposed taxonomy,
discussing how certain design choices lead to different patterns in performance
and cost. We also outline theoretical underpinnings, relationships between
prompting and other parts of the LLM ecosystem such as knowledge bases, and the
associated research challenges. Our work will help to advance future prompt
engineering techniques.
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