# Towards better Human-Agent Alignment: Assessing Task Utility in LLM-Powered Applications

arXiv: https://arxiv.org/abs/2402.09015 · HTML: https://mlsanity.com/paper/2402.09015/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 29 of its 53 references that are in the map.
- First posted on arXiv 14 Feb 2024.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 29 of 29)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Measuring Mathematical Problem Solving With the MATH Dataset](https://mlsanity.com/paper/2103.03874/) (2021)
- [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [PubMedQA: A Dataset for Biomedical Research Question Answering](https://mlsanity.com/paper/1909.06146/) (2019)
- [ALFWorld: Aligning Text and Embodied Environments for Interactive Learning](https://mlsanity.com/paper/2010.03768/) (2020)
- [GAIA: a benchmark for General AI Assistants](https://mlsanity.com/paper/2311.12983/) (2023)
- [Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate](https://mlsanity.com/paper/2305.19118/) (2023)
- [ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks](https://mlsanity.com/paper/1912.01734/) (2019)
- [Can Large Language Models Be an Alternative to Human Evaluations?](https://mlsanity.com/paper/2305.01937/) (2023)
- [Aligning AI With Shared Human Values](https://mlsanity.com/paper/2008.02275/) (2020)
- [Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM Agents](https://mlsanity.com/paper/2306.03314/) (2023)
- [TextWorld: A Learning Environment for Text-based Games](https://mlsanity.com/paper/1806.11532/) (2018)
- [On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective](https://mlsanity.com/paper/2302.12095/) (2023)
- [Evaluating Large Language Models: A Comprehensive Survey](https://mlsanity.com/paper/2310.19736/) (2023)
- [Do LLMs exhibit human-like response biases? A case study in survey design](https://mlsanity.com/paper/2311.04076/) (2023)
- [What makes a good conversation? How controllable attributes affect human judgments](https://mlsanity.com/paper/1902.08654/) (2019)
- [Multi-Dimensional Evaluation of Text Summarization with In-Context Learning](https://mlsanity.com/paper/2306.01200/) (2023)
- [Goal Representations for Instruction Following: A Semi-Supervised Language Interface to Control](https://mlsanity.com/paper/2307.00117/) (2023)
- [Exploring the Reliability of Large Language Models as Customized Evaluators for Diverse NLP Tasks](https://mlsanity.com/paper/2310.19740/) (2023)
- [Creative Agents: Empowering Agents with Imagination for Creative Tasks](https://mlsanity.com/paper/2312.02519/) (2023)
- [Interactive Grounded Language Understanding in a Collaborative Environment: IGLU 2021](https://mlsanity.com/paper/2205.02388/) (2022)
- [Aligning Offline Metrics and Human Judgments of Value for Code Generation Models](https://mlsanity.com/paper/2210.16494/) (2022)
- [Semantic-Oriented Unlabeled Priming for Large-Scale Language Models](https://mlsanity.com/paper/2202.06133/) (2022)
- [From Voices to Validity: Leveraging Large Language Models (LLMs) for Textual Analysis of Policy Stakeholder Interviews](https://mlsanity.com/paper/2312.01202/) (2023)
- [DEUS: A Data-driven Approach to Estimate User Satisfaction in Multi-turn Dialogues](https://mlsanity.com/paper/2103.01287/) (2021)
- [Evaluating Personal Assistants on Mobile devices](https://mlsanity.com/paper/1706.04524/) (2017)
- [Optimizing Interactive Systems via Data-Driven Objectives](https://mlsanity.com/paper/2006.12999/) (2020)

## Built on by (most cited first, 5 of 5)

- [Exploring Autonomous Agents through the Lens of Large Language Models: A Review](https://mlsanity.com/paper/2404.04442/) (2024)
- [VeriLA: A Human-Centered Evaluation Framework for Interpretable Verification of LLM Agent Failures](https://mlsanity.com/paper/2503.12651/) (2025)
- [Reflect-RL: Two-Player Online RL Fine-Tuning for LMs](https://mlsanity.com/paper/2402.12621/) (2024)
- [IDAT: A Multi-Modal Dataset and Toolkit for Building and Evaluating Interactive Task-Solving Agents](https://mlsanity.com/paper/2407.08898/) (2024)
- [CollabSkill: Evaluating Human-Agent Collaboration On Real-World Tasks](https://mlsanity.com/paper/2606.09833/) (2026)

## Abstract (arXiv)

The rapid development in the field of Large Language Models (LLMs) has led to a surge in applications that facilitate collaboration among multiple agents to assist humans in their daily tasks. However, a significant gap remains in assessing whether LLM-powered applications genuinely enhance user experience and task execution efficiency. This highlights the pressing need for methods to verify utility of LLM-powered applications, particularly by ensuring alignment between the application's functionality and end-user needs. We introduce AgentEval provides an implementation for the math problems, 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 robustness of quantifier's work.