How can I make my LLM agents safer and more explainable?
xSRL: Safety-Aware Explainable Reinforcement Learning - Safety as a Product of Explainability
This paper introduces xSRL, a framework for explaining the behavior and safety of reinforcement learning (RL) agents, particularly in critical applications. It combines local explanations (specific state-action insights) with global explanations (overall strategy summaries) to provide a more comprehensive understanding of agent decisions, especially regarding safety constraints. xSRL also incorporates adversarial explanations, enabling developers to identify and patch vulnerabilities without retraining the agent.
Key points for LLM-based multi-agent systems: xSRL's explainability framework could improve transparency and trust in complex multi-agent systems. Adversarial explanations can aid in debugging and enhancing robustness, a crucial factor in multi-agent systems involving LLMs, which can be susceptible to adversarial attacks. The combination of local and global explanations offers a holistic view of individual agent actions and their impact on overall system behavior, improving the development and control of LLM-driven multi-agent interactions.