This paper proposes a unified framework for analyzing and comparing different LLM reasoning schemes, focusing on chains, trees, and graphs of thoughts.
The paper explores a comprehensive analysis of 30+ existing LLM reasoning schemes
https://arxiv.org/abs/2401.14295
🤔 Original Problem:
→ Existing LLM reasoning schemes lack a standardized way to analyze and compare their structures and effectiveness.
→ There's no clear understanding of how different topologies (chains, trees, graphs) impact reasoning performance.
→ The field lacks a comprehensive taxonomy for classifying and evaluating various prompting techniques.
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🔍 Solution in this Paper:
→ The authors develop a general blueprint for LLM reasoning schemes.
→ They introduce a taxonomy based on topology class, scope, representation, derivation, reasoning schedule, and integration with the AI pipeline.
→ The paper analyzes existing schemes using this framework, highlighting key differences in design and performance.
→ A functional formulation is provided to describe the prompting pipeline and reasoning topologies mathematically.
→ The authors identify fundamental building blocks of LLM reasoning, including thoughts, topologies, and semantic roles.
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💡 Key Insights from this Paper:
→ Reasoning topologies can be classified into chains, trees, and graphs, each with distinct advantages
→ Multi-prompt approaches often outperform single-prompt methods for complex tasks
→ Explicit topology representations tend to be more effective than implicit ones
→ The choice of reasoning schedule (e.g., BFS, DFS) significantly impacts performance
→ Integration with other AI pipeline components (e.g., retrieval, tools) enhances reasoning capabilities
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📊 Results:
→ The paper doesn't provide specific quantitative results
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→ The proposed taxonomy enables systematic comparison of different approaches
→ The framework reveals patterns in performance across various reasoning tasks
A unified lens to dissect LLM reasoning: chains, trees, and graphs demystified.
LLM thinking, decoded: from simple chains to complex graphs, all in one framework.
Cracking the code of LLM reasoning: a roadmap from chains to graphs.
One framework to rule them all: decoding LLM reasoning from chains to graphs.
Chains, trees, graphs: the secret sauce of LLM thinking, now in plain English.
Think your LLM's smart? Wait till you see its thought map!
From simple chains to brainy graphs: how LLMs really think.
Untangling the spaghetti of LLM reasoning, one topology at a time.
