Assessing and Verifying Task Utility in LLM -Powered Applications (2024)

Abstract: The rapid development of Large Language Models (LLMs) has led to a surge in applications that facilitate collaboration among multiple agents, assisting humans in their daily tasks. However, a significant gap remains in assessing to what extent LLM-powered applications genuinely enhance user experience and task execution efficiency. This highlights the need to verify utility of LLM-powered applications, particularly by ensuring alignment between the application’s functionality and end-user needs. We introduce AgentEval, a novel framework designed to simplify the utility verification process by automatically proposing a set of criteria tailored to the unique purpose of any given application. This allows for a comprehensive assessment, quantifying the utility of an application against the suggested criteria. We present a comprehensive analysis of the effectiveness and robustness of AgentEval for two open source datasets including Math Problem solving and ALFWorld House-hold related tasks. For reproducibility purposes, we make the data, code and all the logs publicly available at https://github.com/Narabzad/AgentEval

## introduction one of the long-lasting goals for intelligent agents @xcite is for them to seamlessly interact with humans in natural language and help their end-users with their tasks, such as completing household tasks, math tutoring, and so on. the rapid development of open-source libraries @xcite helps that goal by simplifying the development of llm-powered agentic applications for various user-centered tasks @xcite @xcite @xcite . to ensure that the application's behavior meets the requirements of the application to bridge this gap, we introduce agenteval, a framework to gauge the utility of llm-powered applications. its goal is to assess the utility by providing application developers with insights into how the current flow can be characterized. agen-teval builds on recent work showing that llms can be a scalable and cost-effective alternative to human evaluation for open-ended tasks @xcite . agenteval as illustrated in fig. 1 , consists of the three following agents, formally defined in sec. 3: (1) criticagent suggests the list of criteria based on the task description and a pair of solutions, where one is preferred over the other one (e.g., successful and failed examples). for instance, for math problems, the criteria could be be efficiency and clarity of the proposed solution; in summary, our main contributions are c1 introducing agenteval, a novel framework that leverages llm-powered agents as a scalable and costeffective alternative to human evaluations, to produce task utility through the collaboration of crit-icagent, quantifieragent and verifieragent; and c2 an in-depth analysis of agenteval robustness for two applications across different solutions, that can be replicated on an unseen domain. ## datasets and solutions this section provides an overview of the datasets utilized in our study i.e., math problem solving and alfworld household task. the math dataset is chosen for its widespread usage and complex problem-solving scenarios that are fundamental in evaluating the effectiveness. alfworld dataset offers a scenario involving multi-turn interactions within a moderately approximated multi-modal environment. each dataset plays a critical role in evaluating different aspects of agenteval's capabilities, from handling complex theoretical problems to navigating real-world scenarios. in both tasks, although success is clearly defined, multiple solutions exist for accomplishing the objectives. an example of math problem solving and alfworld task is shown in appendix a.1. due to space, we report all experiments about math problem solving in the main paper and we keep all the experiments related to alfworld dataset in the appendix a.3. ## robustness analysis and verification in this section, we first analyze the robustness of agenteval, then further investigate how verifieragent can increase the stability of our assessment. ## conclusions and future work we introduced the agenteval framework, designed to swiftly gauge the utility of arbitrary llmpowered agentic applications. our framework leverages recent findings suggesting llms as a scalable and cost-effective alternative to human evaluations for open-ended tasks. agenteval consists of three agents: criticagent suggests criteria based on task descriptions and executions of the applications, quantifieragent quantifies how well the application flow aligns with these crite-ria, and verifieragent modifies the list of criteria if needed. this framework is customizable, adaptable, and can operate in various modes, employing combinations of llms, human inputs, and tools. we believe that suggested agenteval's utility extends beyond immediate performance. it can uncover new system capabilities over time and adapt to changes in user needs tracked by developers. agenteval can also enable developers to assess the alignment between application behavior and suggested user requirements, providing them with insights into areas for improvement. in summary, our contributions include introducing the agenteval framework, and conducting a robust analysis of its performance across various datasets and baselines. agenteval represents a significant step towards assessing llm-powered applications. secondly, our experiments were conducted exclusively with closed-source llms, specifically with gpt-4. this may limit the generalizability of our findings. plans to include a broader array of llms, including open-source models, are considered for future studies to validate and possibly enhance the robustness of our conclusions. additionally, the tests conducted were limited to specific scenarios within math problem solving and household tasks. expanding the diversity of test scenarios could help in understanding the broader applicability of the framework. thirdly, while agenteval employs a novel methodology leveraging llms to estimate utility, the absence of human evaluation in our validation process could be viewed as a drawback. human evaluations provide unique insights, especially in subjective aspects of utility that automated systems might overlook. however, such evaluations are often cost-prohibitive and logistically challenging, restricting our ability to implement them within this study. especially do developers of agentic llm-powered applications who needs insights fast as they go with the deployments. lastly, as llm technologies evolve, the criteria and metrics used for evaluation may need to be updated or revised. what works for assessing current llms may not hold as these models become more advanced. continuous updates to the evaluation framework will be necessary to keep pace with technological advancements.