Formally Specifying the High-Level Behavior of LLM-Based Agents
Maxwell Crouse, Ibrahim Abdelaziz, Ramon Astudillo, Kinjal Basu, Soham, Dan, Sadhana Kumaravel, Achille Fokoue, Pavan Kapanipathi, Salim Roukos, Luis, Lastras
TL;DR
This paper introduces a minimalistic, declarative framework for specifying and constructing high-level behaviors of LLM-based agents, simplifying their design and enabling rapid experimentation.
Contribution
It proposes a high-level, declarative specification approach for LLM agents that guarantees desired behaviors through decoding monitors, facilitating flexible and rapid agent development.
Findings
Framework enables rapid design and testing of LLM agents.
Demonstrated implementation of existing and new agents like PASS.
Outperforms other agents on reasoning benchmarks.
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…
Peer Reviews
Decision·ICLR 2024 Conference Withdrawn Submission
* The declarative approach simplifies behavior specification, and the constrained decoder ensures accurate alignment of generated output with desired behaviors. * The paper sets up a new way based on formal logic to control the high-level behavior of an LLM-based agent at runtime. * The experiment results demonstrate the utility of the framework on three datasets and show that hard constraints imposed on generation can lead to an increase in agent performance.
* The paper asserts the capability to formally validate prompt examples by the proposed approach. However, I was unable to locate evidence supporting this claim. * The experiment conducted falls short of demonstrating the value of integrating Linear Temporal Logic (LTL) specifications with Language Learning Models (LLMs). The evaluation exclusively focuses on the ReACT agent framework, where the agent operates within a loop involving generative and tool execution steps, resulting in behavior re
1. The authors endeavor to present a more rigorous formulation of agent prompting in the context of LLM, which I appreciate as practices in this domain are still quite experimental and require standardization. 2. The paper has fair writing, which introduces the concept of LTL clearly.
1. Lack of Solid Contribution: while the concept of LTL is interesting, it is not an original idea of this paper. Further, the application of LTL in this paper seems unnecessary. The main technical proposal in this work--constrained decoding--has been a well-established practice in improving LM's structured generation. It has very weak connection with the LTL's formulation and so many logical symbols introduced, which occupies more than half of the method content. 2. Lack of Strong Experiment Re
- Originality: To my knowledge this is the first attempt at an overarching formal framework for specifying the architectures of LLM-based agents. - Quality - Formal logic, and in particular LTL, are well-suited to specifying the architecture of LLM agents. - The pipeline is simple and intuitive, so it seems extremely easy to use and good for fast iteration of architectures. - The pipeline is expressive enough to capture the architecture of at least a few popular LLM age
- I think the experimental results slightly undermine the significance of the paper's contribution. The performance improvement obtained using the pipeline made decreased with the size of the LLM. Since there are much larger LLMs than the ones used in these experiments, and we can expect to generally be working with larger models as time goes on, it seems constraining models using LTL may not be especially useful for improving performance - LLMs understand what trajectories of states are valid a
1. I think it is an extremely interesting idea to use LTL specifications for constraining the behavior of not only LLM-based agents but also of LLMs in general. While there is a growing body of work on constrained decoding for LLMs, using LTL to express these constraints has not been explored and I believe this is a fruitful direction to explore. 2. The fact that the presented framework can be used to express a number of LLM-based agents (ReACT, CoT, Reflexion, Chatbots) suggests the generality
The primary weakness of the paper is the sparsity and lack of precision regarding the technical details about the framework. I list my concerns below: 1. The transition system based formalization of the agent behavior (Section 3.2) is imprecise. What is the precise notion of a state? Does a state include a prompt string (such as "Thought:") along with the generated string? Does a state always need to start with a special string such as "Thought"? Also, to use LTL, each state needs to be associa
+ The paper address an important problem + It is easy to follow
- The novelty and the contributions of the paper are unclear - The performance of the method seems limited
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Taxonomy
TopicsMulti-Agent Systems and Negotiation · Topic Modeling · Semantic Web and Ontologies