选项流：通过思考选项实现多样化与提升的大语言模型推理

摘要

我们提出了一种名为“选项流”（Flow-of-Options, FoO）的创新推理方法，旨在解决大型语言模型（LLMs）中的固有偏差。FoO使LLMs能够在推理过程中系统地探索多种可能性，这一点通过一个基于FoO的自主解决机器学习任务（AutoML）的代理系统得到了验证。我们的框架在标准数据科学任务上实现了38.2%至69.2%的性能提升，在治疗化学任务上提升了37.4%至47.9%，超越了现有最先进的基线模型。每项任务的总操作成本低于1美元，我们的框架非常适合成本敏感型应用。除了分类和回归任务，我们还展示了基于FoO的代理系统在强化学习和图像生成等任务中的广泛适用性。与当前最先进的AutoML代理系统相比，我们的框架取得了显著进步，这得益于FoO通过压缩、可解释的表示强制LLM解决方案的多样性，并结合基于案例的推理支持长期记忆。

English

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.

选项流：通过思考选项实现多样化与提升的大语言模型推理

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

摘要

Summary

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