mlsanityData to 7 Oct 2026

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

arXiv 2402.09015 ↗First posted 14 Feb 2024cs.CL cs.AIcited by 5

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

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.

Key facts. Cited by 5 papers in the map (data through 7 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.

Builds on

29 of 53 references.

Closest ancestors · 3

Same direction, newest first

  1. 2023Creative Agents: Empowering Agents with Imagination for Creative Tasks5 Dec
  2. 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework1 Aug
  3. 2023Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM Agents5 Jun

Other references · 26

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  3. 2021Measuring Mathematical Problem Solving With the MATH Datasetcited by 5,203
  4. 2016MS MARCO: A Human Generated MAchine Reading COmprehension Datasetcited by 1,591
  5. 2019PubMedQA: A Dataset for Biomedical Research Question Answeringcited by 1,271
  6. 2020ALFWorld: Aligning Text and Embodied Environments for Interactive Learningcited by 1,143
  7. 2023GAIA: a benchmark for General AI Assistantscited by 1,084
  8. 2023Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debatecited by 995
  9. 2019ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Taskscited by 773
  10. 2023Can Large Language Models Be an Alternative to Human Evaluations?cited by 729
  11. 2020Aligning AI With Shared Human Valuescited by 706
  12. 2018TextWorld: A Learning Environment for Text-based Gamescited by 304
14 more
  1. 2023On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspectivecited by 208
  2. 2023Evaluating Large Language Models: A Comprehensive Surveycited by 200
  3. 2023Do LLMs exhibit human-like response biases? A case study in survey designcited by 176
  4. 2019What makes a good conversation? How controllable attributes affect human judgmentscited by 140
  5. 2023Multi-Dimensional Evaluation of Text Summarization with In-Context Learningcited by 44
  6. 2023Goal Representations for Instruction Following: A Semi-Supervised Language Interface to Controlcited by 32
  7. 2022Interactive Grounded Language Understanding in a Collaborative Environment: IGLU 2021cited by 23
  8. 2023Exploring the Reliability of Large Language Models as Customized Evaluators for Diverse NLP Taskscited by 23
  9. 2022Aligning Offline Metrics and Human Judgments of Value for Code Generation Modelscited by 14
  10. 2022Semantic-Oriented Unlabeled Priming for Large-Scale Language Modelscited by 12
  11. 2023From Voices to Validity: Leveraging Large Language Models (LLMs) for Textual Analysis of Policy Stakeholder Interviewscited by 8
  12. 2021DEUS: A Data-driven Approach to Estimate User Satisfaction in Multi-turn Dialoguescited by 7
  13. 2017Evaluating Personal Assistants on Mobile devicescited by 3
  14. 2020Optimizing Interactive Systems via Data-Driven Objectivescited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 1; 2026: 1.

5 papers.

  1. 2024Exploring Autonomous Agents through the Lens of Large Language Models: A Reviewcited by 30
  2. 2025VeriLA: A Human-Centered Evaluation Framework for Interpretable Verification of LLM Agent Failurescited by 14
  3. 2024Reflect-RL: Two-Player Online RL Fine-Tuning for LMscited by 13
  4. 2024IDAT: A Multi-Modal Dataset and Toolkit for Building and Evaluating Interactive Task-Solving Agentscited by 1
  5. 2026CollabSkill: Evaluating Human-Agent Collaboration On Real-World Taskscited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2025Position: The Real Barrier to LLM Agent Usability is Agentic ROI
  2. 2024ALI-Agent: Assessing LLMs' Alignment with Human Values via Agent-based Evaluation
  3. 2026UniACE: A Unified Framework for Evaluating LLM Agentic Capabilities
  4. 2025LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey
  5. 2026Auditing Automated Evaluation, Error Propagation, and Runtime Mitigation in Tool-Using Language Agents
  6. 2023AgentBench: Evaluating LLMs as Agents
  7. 2024Can Large Language Models be Trusted for Evaluation? Scalable Meta-Evaluation of LLMs as Evaluators via Agent Debate
  8. 2023AgentSims: An Open-Source Sandbox for Large Language Model Evaluation