mlsanityData to 7 Oct 2026

EvalLM: Interactive Evaluation of Large Language Model Prompts on User-Defined Criteria

arXiv 2309.13633 ↗First posted 24 Sep 2023cs.HC cs.AI cs.CLcited by 73

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Abstract (arXiv)

By simply composing prompts, developers can prototype novel generative applications with Large Language Models (LLMs). To refine prototypes into products, however, developers must iteratively revise prompts by evaluating outputs to diagnose weaknesses. Formative interviews (N=8) revealed that developers invest significant effort in manually evaluating outputs as they assess context-specific and subjective criteria. We present EvalLM, an interactive system for iteratively refining prompts by evaluating multiple outputs on user-defined criteria. By describing criteria in natural language, users can employ the system's LLM-based evaluator to get an overview of where prompts excel or fail, and improve these based on the evaluator's feedback. A comparative study (N=12) showed that EvalLM, when compared to manual evaluation, helped participants compose more diverse criteria, examine twice as many outputs, and reach satisfactory prompts with 59% fewer revisions. Beyond prompts, our work can be extended to augment model evaluation and alignment in specific application contexts.

Key facts. Cited by 73 papers in the map (data through 7 Oct 2026). Builds on 59 of its 81 references that are in the map. First posted on arXiv 24 Sep 2023.

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59 of 81 references.

References · 59

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38 more
  1. 2021Calibrate Before Use: Improving Few-Shot Performance of Language Modelscited by 1,371
  2. 2021What Makes Good In-Context Examples for GPT-3?cited by 1,184
  3. 2018Hierarchical Neural Story Generationcited by 1,182
  4. 2023PaLM 2 Technical Reportcited by 1,154
  5. 2021Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivitycited by 1,122
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Citing papers per year: 2024: 27; 2025: 27; 2026: 19.

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  1. 2024Who Validates the Validators? Aligning LLM-Assisted Evaluation of LLM Outputs with Human Preferencescited by 142
  2. 2024Prometheus-Vision: Vision-Language Model as a Judge for Fine-Grained Evaluationcited by 123
  3. 2024A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendationscited by 84
  4. 2024Roleplay-doh: Enabling Domain-Experts to Create LLM-simulated Patients via Eliciting and Adhering to Principlescited by 72
  5. 2024LLM-based NLG Evaluation: Current Status and Challengescited by 71
  6. 2025Interactive Debugging and Steering of Multi-Agent AI Systemscited by 68
  7. 2024LAVE: LLM-Powered Agent Assistance and Language Augmentation for Video Editingcited by 47
  8. 2024Leveraging Large Language Models for NLG Evaluation: Advances and Challengescited by 37
  9. 2024Are LLM-based Evaluators Confusing NLG Quality Criteria?cited by 35
  10. 2024How Far Are We From AGI: Are LLMs All We Need?cited by 31
  11. 2024LLM Comparator: Visual Analytics for Side-by-Side Evaluation of Large Language Modelscited by 29
  12. 2024Learning to Refine with Fine-Grained Natural Language Feedbackcited by 27
38 more
  1. 2024TeachTune: Reviewing Pedagogical Agents Against Diverse Student Profiles with Simulated Studentscited by 25
  2. 2024What Should We Engineer in Prompts? Training Humans in Requirement-Driven LLM Usecited by 24
  3. 2025CUPID: Evaluating Personalized and Contextualized Alignment of LLMs from Interactionscited by 20
  4. 2024SPADE: Synthesizing Data Quality Assertions for Large Language Model Pipelinescited by 19
  5. 2024GhostWriter: Augmenting Collaborative Human-AI Writing Experiences Through Personalization and Agencycited by 18
  6. 2024What Makes a Good Story and How Can We Measure It? A Comprehensive Survey of Story Evaluationcited by 17
  7. 2025Understanding the LLM-ification of CHI: Unpacking the Impact of LLMs at CHI through a Systematic Literature Reviewcited by 16
  8. 2024Position: Towards Bidirectional Human-AI Alignmentcited by 15
  9. 2025VeriLA: A Human-Centered Evaluation Framework for Interpretable Verification of LLM Agent Failurescited by 14
  10. 2024Beyond the Comfort Zone: Emerging Solutions to Overcome Challenges in Integrating LLMs into Software Productscited by 11
  11. 2025SPHERE: An Evaluation Card for Human-AI Systemscited by 9
  12. 2024Mitigating Exaggerated Safety in Large Language Modelscited by 7
  13. 2025Just-In-Time Objectives: A General Approach for Specialized AI Interactionscited by 7
  14. 2026DiLLS: Interactive Diagnosis of LLM-based Multi-agent Systems via Layered Summary of Agent Behaviorscited by 7
  15. 2024ChainBuddy: An AI Agent System for Generating LLM Pipelinescited by 6
  16. 2024C2: Scalable Auto-Feedback for LLM-based Chart Generationcited by 6
  17. 2024Self-Improving Customer Review Response Generation Based on LLMscited by 5
  18. 2024Policy Maps: Tools for Guiding the Unbounded Space of LLM Behaviorscited by 5
  19. 2025KOFFVQA: An Objectively Evaluated Free-form VQA Benchmark for Large Vision-Language Models in the Korean Languagecited by 5
  20. 2025PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelinescited by 5
  21. 2024Large Language Models Meet User Interfaces: The Case of Provisioning Feedbackcited by 4
  22. 2024Exploring Bengali Religious Dialect Biases in Large Language Models with Evaluation Perspectivescited by 4
  23. 2025Bridging the AI Adoption Gap: Designing an Interactive Pedagogical Agent for Higher Education Instructorscited by 4
  24. 2025ELI-Why: Evaluating the Pedagogical Utility of Language Model Explanationscited by 4
  25. 2025RAGTrace: Understanding and Refining Retrieval-Generation Dynamics in Retrieval-Augmented Generationcited by 4
  26. 2026From Words to Widgets for Controllable LLM Generationcited by 4
  27. 2025DxHF: Providing High-Quality Human Feedback for LLM Alignment via Interactive Decompositioncited by 3
  28. 2025Counterfactual Evaluation for Blind Attack Detection in LLM-based Evaluation Systemscited by 3
  29. 2025Illuminating LLM Coding Agents: Visual Analytics for Deeper Understanding and Enhancementcited by 3
  30. 2025Evalet: Evaluating Large Language Models through Functional Fragmentationcited by 3
  31. 2024Conversational Prompt Engineeringcited by 2
  32. 2025ALIGN: Prompt-based Attribute Alignment for Reliable, Responsible, and Personalized LLM-based Decision-Makingcited by 2
  33. 2025When the Domain Expert Has No Time and the LLM Developer Has No Clinical Expertise: Real-World Lessons from LLM Co-Design in a Safety-Net Hospitalcited by 2
  34. 2025Prototyping Multimodal GenAI Real-Time Agents with Counterfactual Replays and Hybrid Wizard-of-Ozcited by 2
  35. 2025Interpreting LLM-as-a-Judge Policies via Verifiable Global Explanationscited by 2
  36. 2025Generate, Evaluate, Iterate: Synthetic Data for Human-in-the-Loop Refinement of LLM Judgescited by 2
  37. 2026"Bespoke Bots": Diverse Instructor Needs for Customizing Generative AI Classroom Chatbotscited by 2
  38. 2026Who Defines "Best"? Towards Interactive, User-Defined Evaluation of LLM Leaderboardscited by 2

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