Introduction

In recent years, Large Language Models (LLMs) such as GPT-3 and PaLM have taken significant strides in advancing the capabilities of artificial intelligence. These developments are largely attributed to the scaling up of models and the augmentation of training data sizes. However, a persistent question that looms in the background is the ability of these LLMs to reason symbolically, particularly in manipulating symbols based on logical rules. Today, we explore the efforts being made to impart LLMs with a deeper understanding of algorithmic reasoning, a crucial skill in ensuring their further advancement. Dive deeper into this topic here.

Neural Networks and Their Limitations

At the heart of LLMs lie neural networks, potent tools capable of intricate pattern matching. Yet, they are equally susceptible to overfitting to spurious statistical patterns present in data. While this doesn’t hamper performance in scenarios with ample and diverse training data, it becomes a bottleneck when rule-based reasoning, especially arithmetic tasks, comes into play. Despite noticeable advancements in various natural language processing tasks, maintaining accuracy in simple arithmetic operations remains a substantial challenge.

Teaching Algorithmic Reasoning

To address these limitations, researchers have embarked on a journey to enhance the algorithmic reasoning capabilities of LLMs, employing in-context learning as a pivotal tool. This approach emphasizes the model’s ability to perform tasks proficiently after witnessing a few examples within its context, without necessitating weight updates. A remarkable breakthrough in this venture has been the development of a novel algorithmic prompting technique. This technique empowers general-purpose language models to exhibit strong generalization abilities in tackling arithmetic problems, surpassing the complexity of those observed in the prompt.

Algorithmic Prompting as a Skill

To foster the algorithm as a skill in a model, the concept of algorithmic prompting has been introduced. This strategy stands distinct for its ability to elucidate the steps necessary for an algorithmic solution, offering a comprehensive explanation for each step to prevent misinterpretation by the LLM. A prime example of this can be witnessed in the two-number addition task, where the rule of carry is explicitly defined, facilitating the model to focus on pertinent details and interpret the prompt with heightened accuracy.

Testing the Approach

To validate the effectiveness of this approach, a series of tests were conducted using various prompt strategies for addition. Impressively, even with a limited number of prompt examples, the model demonstrated high accuracy in solving addition questions, extending beyond the complexity seen in the prompt. This showcases the model’s prowess in executing an input-agnostic algorithm, a significant milestone in the journey towards achieving superior reasoning performance.

Leveraging Algorithmic Skills as Tool Use

Taking a step further, the study explored the potential of utilizing these newly acquired algorithmic skills in broader reasoning processes, particularly in solving grade school math word problems. By fostering interactions between differently-prompted models specialized in various skills, a strategy was devised to address complex tasks effectively. This innovative approach, as evidenced in the GSM8k-Hard dataset, amplifies the performance by facilitating seamless collaboration between models, thereby revolutionizing the way complex tasks are approached.

Conclusion

This groundbreaking research heralds a new era in the evolution of Large Language Models, shedding light on the potential of transforming longer contexts into enhanced reasoning performance. By nurturing the ability to simulate long contexts and generate more informative rationales, it paves the way for promising research directions, holding the promise of reshaping the future of artificial intelligence.

Acknowledgements

We extend our heartfelt gratitude to the Google Research blog for offering a treasure trove of valuable information, showcasing the insights and expertise of seasoned professionals in the field. We are particularly thankful to co-authors Behnam Neyshabur, Azade Nova, Hugo Larochelle, and Aaron Courville for their invaluable contributions to the original research paper and for sharing their rich insights on the blog. A special note of appreciation goes to Tom Small for creating the vibrant animations that embellished their post, and to Hattie Zhou for her significant contributions during her internship at Google Research. This article is generated based on an input prompt from their enlightening blog post, which serves as a beacon guiding us in the ever-evolving landscape of algorithmic reasoning in large language models.

Original article by Hattie Zhou, Graduate Student at MILA, Hanie Sedghi, Research Scientist, Google, can be found here.