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Position: We Need An Algorithmic Understanding of Generative AI
- A position paper suggests the need for a deeper understanding of the algorithms used by Large Language Models (LLMs), as research focus has mainly been on scale and performance improvement.
- The proposed AlgEval framework aims to investigate the algorithms LLMs learn and utilize, focusing on algorithmic primitives, attention mechanisms, and inference-time computation.
- The framework includes studying the composition of algorithmic primitives to solve specific tasks, with a case study on emergent search algorithms.
- The systematic evaluation of LLMs' problem-solving methods can provide insights into internal reasoning and lead to more efficient training methods and novel architectures.
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