Title: Unlocking Algorithmic Reasoning in Language Models
Language models like GPT-3 and PaLM have made tremendous progress, thanks to larger models and training data. However, there’s an ongoing debate about whether these models can reason symbolically. While they can perform simple arithmetic operations, they struggle with larger numbers, indicating a lack of understanding of the underlying rules for these operations. Neural networks are prone to overfitting and exploiting spurious correlations, which limits their ability to generalize to out-of-distribution tasks. This has hindered progress in arithmetic tasks like addition. To address this, we introduce an approach that leverages in-context learning to enable algorithmic reasoning capabilities in language models.
Algorithmic Prompting for Algorithmic Reasoning:
To teach language models algorithmic reasoning, we develop algorithmic prompting. This approach solves tasks by providing the steps needed for an algorithmic solution and explains each step in detail. We use explicit equations and non-ambiguous formats to ensure accurate interpretation by the model. We demonstrate the effectiveness of algorithmic prompts in maintaining high accuracy even for questions significantly longer than the prompt’s length.
Leveraging Algorithmic Skills:
We evaluate the model’s ability to leverage algorithmic reasoning in solving broader tasks, such as grade school math word problems. By combining models specialized in informal reasoning and addition, we create a collaborative approach where prompts from one model invoke the expertise of the other. This strategy proves effective in tackling complex tasks, as demonstrated by the improved performance on a difficult problem set.
By leveraging in-context learning and algorithmic prompting, we unlock algorithmic reasoning abilities in language models. The results suggest the potential for better reasoning performance by providing more detailed explanations. This opens up promising research directions in simulating long contexts and generating informative rationales.
We would like to express our gratitude to our co-authors and contributors for their valuable input. Special thanks to Tom Small for creating the animations. This work was conducted during Hattie Zhou’s internship at Google Research.