Flow-of-Options: Diversified and Improved LLM Reasoning by Thinking Through Options

• URL: http://arxiv.org/abs/2502.12929v1
• Date: Tue, 18 Feb 2025 15:11:46 GMT
• Title: Flow-of-Options: Diversified and Improved LLM Reasoning by Thinking Through Options
• Authors: Lakshmi Nair, Ian Trase, Mark Kim,
• Abstract summary: Flow-of-Options (FoO) is designed to address intrinsic biases in Large Language Models (LLMs)<n>Our framework outperforms state-of-the-art baselines, achieving improvements of 38.2% - 69.2% on standard data science tasks.<n>With an overall operation cost under $1 per task, our framework is well-suited for cost-sensitive applications.
• Score: 1.2289361708127877
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a novel reasoning approach called Flow-of-Options (FoO), designed to address intrinsic biases in Large Language Models (LLMs). FoO enables LLMs to systematically explore a diverse range of possibilities in their reasoning, as demonstrated by an FoO-based agentic system for autonomously solving Machine Learning tasks (AutoML). Our framework outperforms state-of-the-art baselines, achieving improvements of 38.2% - 69.2% on standard data science tasks, and 37.4% - 47.9% on therapeutic chemistry tasks. With an overall operation cost under $1 per task, our framework is well-suited for cost-sensitive applications. Beyond classification and regression, we illustrate the broader applicability of our FoO-based agentic system to tasks such as reinforcement learning and image generation. Our framework presents significant advancements compared to current state-of-the-art agentic systems for AutoML, due to the benefits of FoO in enforcing diversity in LLM solutions through compressed, explainable representations that also support long-term memory when combined with case-based reasoning.

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