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LM-Guided CoT: A Novel Machine Learning Framework that Leverages a Lightweight (<1B) Language Model (LM) for guiding a black-box large (>10B) LM in Reasoning Tasks

LM-Guided CoT: A Novel Machine Learning Framework that Leverages a Lightweight (<1B) Language Model (LM) for guiding a black-box large (>10B) LM in Reasoning Tasks” width=”500″ height=”auto” /></figure> <h3>LM-Guided CoT: A Novel Machine Learning Framework</h3> <h4>Introduction</h4> <p>Chain-of-thought (CoT) prompting is a method to improve language models’ reasoning abilities. However, it has limitations, especially for smaller models. Recent research proposes LM-guided CoT, a framework that enhances CoT prompting by decomposing it into rationale generation and answer prediction steps optimized with reinforcement learning (RL).</p> <h4>Practical Solutions and Value</h4> <p>The LM-guided CoT framework introduces two language models: a lightweight model (MS) for generating optimal rationales and a large model (ML) for predicting outputs based on these rationales. By applying knowledge distillation and fine-grained measurements, the framework significantly enhances the quality of generated rationales and ultimately improves CoT reasoning performance. The study shows that LM-guided CoT outperforms original CoT prompting and rivals standard prompting in accuracy, especially for questions with lengthy contexts.</p> <h4>AI Implementation Advice</h4> <p>For companies looking to evolve with AI, LM-guided CoT offers a practical machine learning framework that can redefine the way of work. It is important to identify automation opportunities, define KPIs, select an AI solution, and implement gradually to ensure measurable impacts on business outcomes.</p> <p>For AI KPI management advice and continuous insights into leveraging AI, connect with us at <a href=hello@itinai.com or stay tuned on our Telegram channel or Twitter.

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