EPFL and Meta have introduced a new way for AI to perform more complex tasks. The Chain-of-Abstraction (CoA) reasoning method allows large language models (LLMs) to perform multi-step reasoning with the help of tools. This new approach has shown significant improvements in areas like mathematical reasoning and Wikipedia question answering.
The CoA method separates general reasoning from domain-specific knowledge, making it more robust and efficient. By using the CoA method, LLMs have faster inference speeds and better accuracy, making it a promising approach for enhancing performance across various domains. To find out more about this breakthrough, you can read the full research paper here.
It’s exciting to see how this research could lead to improvements across a wide range of real-world applications, and it’s definitely a significant step forward for AI.
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