Summary of Combinatorial Reasoning: Selecting Reasons in Generative Ai Pipelines Via Combinatorial Optimization, by Mert Esencan et al.
Combinatorial Reasoning: Selecting Reasons in Generative AI Pipelines via Combinatorial Optimization
by Mert Esencan, Tarun Advaith Kumar, Ata Akbari Asanjan, P. Aaron Lott, Masoud Mohseni, Can Unlu, Davide Venturelli, Alan Ho
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Emerging Technologies (cs.ET); 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 This paper introduces Combinatorial Reasoning (CR), a fully-automated prompting method that utilizes Large Language Models (LLMs) to perform human-like reasoning tasks. Building upon recent advancements in LLMs, the authors investigate whether Quadratic Unconstrained Binary Optimization (QUBO) solutions can be used to select relevant reasons and construct effective prompts for reasoning tasks. The framework also explores the acceleration of CR using specialized solvers and compares its performance with simpler zero-shot strategies such as linear majority rule or random selection. The study suggests that combining combinatorial solvers with generative AI pipelines is a promising avenue for developing more sophisticated AI reasoning capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how we can make computers think better, like humans do. Currently, computers are very good at doing simple tasks, but they struggle to reason and solve problems the way people do. To improve this, researchers have developed special ways of asking questions to computers, called prompting techniques. But these methods often require human expertise and are not fully automated. This new approach, called Combinatorial Reasoning (CR), uses a computer’s language abilities to generate prompts for reasoning tasks. The authors test different methods to see which one works best and how it can be improved. |
Keywords
» Artificial intelligence » Optimization » Prompting » Zero shot
