Keywords: reasoning, language models, chain of thought
TL;DR: We unleash the reasoning capability of language models, which has been constrained by the maximum size of a single context, by letting them recursively create and utilize multiple contexts.
Abstract: With the recent advances in language models, attempts are being made to apply them to solving multi-step reasoning problems. A major breakthrough in this line of research is to let language models generate intermediate steps, often called Chain of Thought (CoT), before producing a final answer. However, language models have an upper bound on the context size, i.e., the number of input tokens, such as 2048 for the recent GPT-3 and PaLM. Although several thousand tokens are enough to handle various tasks, solving more complex reasoning tasks can require orders of magnitude more tokens. Therefore, the context limit imposes a fundamental limit on the model's reasoning capability. Inspired by human's incredible reasoning ability based on abstraction and recursion, we propose Recursion of Thought (RoT) as a model-agnostic framework with the novel paradigm of teaching a language model to divide and conquer complex problems by recursively creating multiple contexts. Since RoT casts the context-related operations as tokens, a language model can trigger the recursion operations by simply producing the corresponding tokens. On multiple arithmetic and algorithmic reasoning tasks, we demonstrate that RoT dramatically improves the recent large-scale language model GPT-3 to solve extremely complex problems. Moreover, RoT can make tiny, randomly initialized Transformers or LSTMs to solve problems that even humans find daunting.
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