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Stream of Search (SoS): Enhancing Language Models for Problem-Solving
Practical Solutions and Value
Language models often struggle with complex decision-making and forward planning due to lack of exposure to mistakes during training. The Stream of Search (SoS) framework addresses this challenge by enabling language models to learn problem-solving through simulated search processes in language, fostering adaptability and overcoming errors.
Researchers from Stanford University, MIT, and Harvey Mudd have devised a method to teach language models how to search and backtrack, resulting in a 25% accuracy increase and the ability to solve 36% of previously unsolved problems. This showcases that language models can learn to solve problems via search, self-improve, and discover new strategies autonomously.
SoS demonstrates promise for tackling complex real-world tasks and has the potential to redefine sales processes and customer engagement through solutions like the AI Sales Bot from itinai.com/aisalesbot, designed to automate customer interactions 24/7.
If you want to evolve your company with AI, consider the practical steps:
- Identify Automation Opportunities
- Define KPIs for AI Impact
- Select an AI Solution
- Implement Gradually
For more insights and AI KPI management advice, connect with us at hello@itinai.com or stay tuned on our Telegram t.me/itinainews or Twitter @itinaicom.
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