Interpreting Arithmetic Reasoning in Large Language Models using Game-Theoretic Interactions

Neural Information Processing Systems 

In recent years, large language models (LLMs) have made significant advancements in arithmetic reasoning. However, the internal mechanism of how LLMs solve arithmetic problems remains unclear. In this paper, we propose explaining arithmetic reasoning in LLMs using game-theoretic interactions.

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