Testing and Understanding Erroneous Planning in LLM Agents through Synthesized User Inputs

TL;DR

This paper introduces PDoctor, an automated testing framework that detects erroneous planning in LLM-based agents by synthesizing user inputs and formulating constraint satisfaction problems, improving reliability in complex tasks.

Contribution

PDoctor is the first automated approach to test LLM agents' planning by translating user requirements into constraints and detecting errors through constraint solving.

Findings

PDoctor effectively detects diverse planning errors in LLM agents.

It provides valuable insights into error characteristics for developers.

The approach works with multiple agent frameworks and LLMs.

Abstract

Agents based on large language models (LLMs) have demonstrated effectiveness in solving a wide range of tasks by integrating LLMs with key modules such as planning, memory, and tool usage. Increasingly, customers are adopting LLM agents across a variety of commercial applications critical to reliability, including support for mental well-being, chemical synthesis, and software development. Nevertheless, our observations and daily use of LLM agents indicate that they are prone to making erroneous plans, especially when the tasks are complex and require long-term planning. In this paper, we propose PDoctor, a novel and automated approach to testing LLM agents and understanding their erroneous planning. As the first work in this direction, we formulate the detection of erroneous planning as a constraint satisfiability problem: an LLM agent's plan is considered erroneous if its execution violates the constraints derived from the user inputs. To this end, PDoctor first defines a domain-specific language (DSL) for user queries and synthesizes varying inputs with the assistance of the Z3 constraint solver. These synthesized inputs are natural language paragraphs that specify the requirements for completing a series of tasks. Then, PDoctor derives constraints from these requirements to form a testing oracle. We evaluate PDoctor with three mainstream agent frameworks and two powerful LLMs (GPT-3.5 and GPT-4). The results show that PDoctor can effectively detect diverse errors in agent planning and provide insights and error characteristics that are valuable to both agent developers and users. We conclude by discussing potential alternative designs and directions to extend PDoctor.