arXiv:2408.16081v1 Announce Type: new
Abstract: We introduce the Logic-Enhanced Language Model Agents (LELMA) framework, a novel approach to enhance the trustworthiness of social simulations that utilize large language models (LLMs). While LLMs have gained attention as agents for simulating human behaviour, their applicability in this role is limited by issues such as inherent hallucinations and logical inconsistencies. LELMA addresses these challenges by integrating LLMs with symbolic AI, enabling logical verification of the reasoning generated by LLMs. This verification process provides corrective feedback, refining the reasoning output. The framework consists of three main components: an LLM-Reasoner for producing strategic reasoning, an LLM-Translator for mapping natural language reasoning to logic queries, and a Solver for evaluating these queries. This study focuses on decision-making in game-theoretic scenarios as a model of human interaction. Experiments involving the Hawk-Dove game, Prisoner’s Dilemma, and Stag Hunt highlight the limitations of state-of-the-art LLMs, GPT-4 Omni and Gemini 1.0 Pro, in producing correct reasoning in these contexts. LELMA demonstrates high accuracy in error detection and improves the reasoning correctness of LLMs via self-refinement, particularly in GPT-4 Omni.
Enhancing Trustworthiness in Social Simulations with the LELMA Framework
Social simulations that utilize large language models (LLMs) have gained popularity in recent years for simulating human behavior. However, these simulations often suffer from issues such as hallucinations and logical inconsistencies. To address these challenges, the Logic-Enhanced Language Model Agents (LELMA) framework was introduced as a novel approach to enhance the trustworthiness of LLM-based social simulations.
The LELMA framework integrates LLMs with symbolic AI, enabling logical verification of the reasoning generated by the language models. This verification process provides corrective feedback, refining the reasoning output. The framework consists of three main components:
- LLM-Reasoner: This component is responsible for producing strategic reasoning using the large language models.
- LLM-Translator: The LLM-Translator maps the natural language reasoning generated by the LLMs to logic queries, making it easier to evaluate the reasoning.
- Solver: The Solver evaluates the logic queries and provides feedback on their correctness.
The main focus of this study is decision-making in game-theoretic scenarios, which serves as a model for human interaction. The experiments conducted using the Hawk-Dove game, Prisoner’s Dilemma, and Stag Hunt game highlight the limitations of state-of-the-art LLMs, such as GPT-4 Omni and Gemini 1.0 Pro, in producing correct reasoning in these specific contexts.
LELMA demonstrates a high level of accuracy in error detection and proves to be effective in improving the reasoning correctness of LLMs through self-refinement. It is particularly successful in refining the reasoning of GPT-4 Omni, showcasing the multi-disciplinary nature of the framework.
The integration of symbolic AI with LLMs in the LELMA framework is a significant step towards addressing the limitations and improving the trustworthiness of LLM-based social simulations. By incorporating logical verification and corrective feedback, LELMA offers a method to refine the reasoning and enhance the reliability of language models in simulating human behavior. This has implications in various fields, including social sciences, computer science, and artificial intelligence.
Next Steps and Future Directions
The LELMA framework opens up several avenues for further research and development:
- Expansion to other domains: While this study focuses on game-theoretic scenarios, the LELMA framework can be extended to other domains to study different aspects of human behavior and decision-making. For example, applying LELMA to economic simulations or social network analysis could provide valuable insights into real-world phenomena.
- Integration of additional reasoning techniques: The current LELMA framework utilizes logical reasoning for verification and refinement. However, integrating other reasoning techniques, such as probabilistic reasoning or causal reasoning, could further enhance the capabilities and accuracy of LLM-based simulations.
- Exploration of ethical considerations: As LLMs become more powerful and their use in social simulations expands, it is crucial to explore the ethical implications of these technologies. Research on ethical guidelines, bias mitigation, and transparency in LLM-based simulations will be essential to ensure responsible and unbiased use of these models.
Overall, the LELMA framework represents a significant advancement in enhancing the trustworthiness of LLM-based social simulations. By combining the strengths of LLMs and symbolic AI, LELMA provides a platform for more accurate and reliable simulations of human behavior, with implications spanning across multiple disciplines.