Large Language Models Are Neurosymbolic Reasoners

This paper explores using Large Language Models (LLMs) as symbolic reasoners in text-based games, achieving 88% average task performance.

Summary:

The article investigates the potential application of Large Language Models (LLMs) as symbolic reasoners in text-based games. The LLM agent is designed to tackle symbolic tasks, including math, map reading, sorting, and applying common sense in text-based worlds. The experimental results demonstrate that the LLM agent significantly enhances the capability of LLMs as automated agents for symbolic reasoning, achieving an average performance of 88% across all tasks.

Major Findings:

1. Text-based games serve as significant benchmarks for agents with natural language capabilities and have garnered substantial attention in the realm of language-centric machine learning research.
2. The challenges in text-based games involving symbolic tasks necessitate interactive multi-step reasoning, and the proposed LLM agent demonstrates superior performance compared to strong baselines, achieving an average performance of 88% across all tasks.
3. The incorporation of external symbolic modules by the LLM agent leads to enhanced average accuracy compared to other baselines, demonstrating the potential of LLMs in performing symbolic reasoning tasks.

Analysis and Critique:

The article provides valuable insights into the effective application of LLMs as neurosymbolic reasoners in text-based games involving symbolic tasks. However, several limitations and areas for further exploration can be identified:

1. Complexity of Tasks: While the LLM agent demonstrates strong performance, it faces challenges in certain tasks such as MapReader and Sorting, indicating limitations in its understanding and memory capacity. This highlights the need for further development to address the complexities of diverse scenarios.

2. Methodological Limitations: The article acknowledges the need for more detailed prompts to offer greater control over the actions of the LLM agent. This limitation suggests a potential area for improvement in the prompting approach to enhance the system’s performance.

3. Generalization and Uncertainty: The LLM agent’s capability to connect with a symbolic module for specific tasks still exhibits uncertainty and is prone to making mistakes, indicating the need for further exploration to improve its generalization and decision-making abilities.

4. Scope for Future Research: Integrating more sophisticated symbolic modules and extending the model’s application to more complex domains are highlighted as potential areas for future research to enhance the LLM agent’s problem-solving approach.

In conclusion, while the article presents promising findings regarding the application of LLMs as neurosymbolic reasoners, it also underscores the need for addressing the limitations identified and conducting further research to fully leverage the potential of LLMs in performing symbolic reasoning tasks in real-world applications.

Appendix

Model	gpt-3.5-turbo-1106
Date Generated	2024-02-26
Abstract	http://arxiv.org/abs/2401.09334v1
HTML	https://browse.arxiv.org/html/2401.09334v1
Truncated	False
Word Count	7175