Formally Specifying the High-Level Behavior of LLM-Based Agents (2310.08535v3)

Abstract: Autonomous, goal-driven agents powered by LLMs have recently emerged as promising tools for solving challenging problems without the need for task-specific finetuned models that can be expensive to procure. Currently, the design and implementation of such agents is ad hoc, as the wide variety of tasks that LLM-based agents may be applied to naturally means there can be no one-size-fits-all approach to agent design. In this work we aim to alleviate the difficulty of designing and implementing new agents by proposing a minimalistic generation framework that simplifies the process of building agents. The framework we introduce allows the user to define desired agent behaviors in a high-level, declarative specification that is then used to construct a decoding monitor which guarantees the LLM will produce an output exhibiting the desired behavior. Our declarative approach, in which the behavior is described without concern for how it should be implemented or enforced, enables rapid design, implementation, and experimentation with different LLM-based agents. We demonstrate how the proposed framework can be used to implement recent LLM-based agents (e.g., ReACT), and show how the flexibility of our approach can be leveraged to define a new agent with more complex behavior, the Plan-Act-Summarize-Solve (PASS) agent. Lastly, we demonstrate that our method outperforms other agents on multiple popular reasoning-centric question-answering benchmarks.

Formally Specifying the High-Level Behavior of LLM-Based Agents

Overview of the Proposed Framework

The recent advancements in goal-driven agents powered by LLMs have shown significant promise in solving complex tasks without necessitating task-specific models. However, designing these agents often involves an ad hoc process due to the diverse tasks they can perform, leading to challenges in implementation. This paper introduces a framework aimed at simplifying the process of building LLM-based agents through a minimalistic generation framework. The proposed framework utilizes a high-level, declarative specification allowing users to define desired agent behaviors, which are then translated into a decoding monitor ensuring the LLM outputs adhere to these specifications.

Key Contributions

The primary contributions of this work are threefold:

1. Introduction of a Declarative Framework: A significant contribution of this paper is the introduction of a declarative framework that simplifies agent design and implementation. Users can specify agent behaviors in a high-level, descriptive manner, and the framework generates a decoding monitor that enforces these behaviors during the agent's operation.
2. Novel Agent Architecture - PASS: The paper introduces the Plan-Act-Summarize-Solve (PASS) agent, showcasing the framework's flexibility. Unlike existing agents that primarily follow sequential or parallel execution steps, PASS dynamically adjusts between these modes, yielding improved performance on reasoning-centric benchmarks.
3. Empirical Evaluation Across Multiple Benchmarks: The authors present a comprehensive evaluation of the proposed framework and the PASS agent across three standard datasets (Hotpot QA, TriviaQA, and GSM8K). The PASS agent, in particular, demonstrates superior or comparable performance against other agents, underlining the framework's efficacy.

Theoretical and Practical Implications

The theoretical implications of this research hinge on the shift towards a high-level specification of agent behaviors in LLM-based systems. The paper challenges the current norm of ad hoc agent design with a structured approach that can cater to a wide variety of tasks without extensive modifications.

From a practical standpoint, the framework significantly reduces the barrier to implementing LLM-based agents by abstracting away the complexities involved in ensuring agent outputs align with desired behaviors. This can accelerate the development of sophisticated LLM-based applications and potentially foster innovation in the use of LLMs for goal-driven tasks.

Future Directions in AI Research

The introduction of a declarative framework for specifying agent behaviors opens up several avenues for future research. One potential direction is the exploration of more complex behavior specifications that could enable agents to handle even more nuanced and varied tasks. Additionally, investigating the integration of this framework with multi-agent systems could offer fascinating insights into how individual agent behaviors contribute to collective outcomes in complex environments.

Moreover, the success of the PASS agent suggests that there exists untapped potential in hybrid execution models that dynamically switch between parallel and sequential steps based on context. Further research could aim at understanding the underlying principles that govern the effectiveness of such models and how they can be leveraged across different domains.

Conclusion

This paper presents a groundbreaking shift toward formalizing the design and implementation of LLM-based agents through a declarative framework. By enabling high-level behavior specification and ensuring compliance through a decoding monitor, the authors address a significant challenge in the practical application of LLM-based agents. The PASS agent exemplifies the strengths of this approach, offering a promising direction for future explorations in the field.