mlsanityData to 14 Oct 2026

A Survey on Evaluation of Large Language Models

arXiv 2307.03109 ↗First posted 6 Jul 2023cs.CL cs.AIcited by 1,242

Not on the map. Why?

Cited by 124 in LLM agents and tool use, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications. As LLMs continue to play a vital role in both research and daily use, their evaluation becomes increasingly critical, not only at the task level, but also at the society level for better understanding of their potential risks. Over the past years, significant efforts have been made to examine LLMs from various perspectives. This paper presents a comprehensive review of these evaluation methods for LLMs, focusing on three key dimensions: what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide an overview from the perspective of evaluation tasks, encompassing general natural language processing tasks, reasoning, medical usage, ethics, educations, natural and social sciences, agent applications, and other areas. Secondly, we answer the `where' and `how' questions by diving into the evaluation methods and benchmarks, which serve as crucial components in assessing performance of LLMs. Then, we summarize the success and failure cases of LLMs in different tasks. Finally, we shed light on several future challenges that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to researchers in the realm of LLMs evaluation, thereby aiding the development of more proficient LLMs. Our key point is that evaluation should be treated as an essential discipline to better assist the development of LLMs. We consistently maintain the related open-source materials at: https://github.com/MLGroupJLU/LLM-eval-survey.

Key facts. Cited by 1,242 papers in the map (data through 14 Oct 2026). Builds on 180 of its 306 references that are in the map. First posted on arXiv 6 Jul 2023.

Builds on

180 of 306 references.

References · 180

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2020Language Models are Few-Shot Learnerscited by 32,419
  4. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  5. 2022Training language models to follow instructions with human feedbackcited by 15,971
  6. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  7. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  8. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  9. 2020Measuring Massive Multitask Language Understandingcited by 7,963
  10. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  11. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
  12. 2021Measuring Mathematical Problem Solving With the MATH Datasetcited by 5,371
38 more
  1. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,326
  2. 2017On Calibration of Modern Neural Networkscited by 4,960
  3. 2018GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understandingcited by 4,899
  4. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  5. 2021On the Opportunities and Risks of Foundation Modelscited by 4,033
  6. 2017Deep reinforcement learning from human preferencescited by 3,912
  7. 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,512
  8. 2021TruthfulQA: Measuring How Models Mimic Human Falsehoodscited by 2,975
  9. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,955
  10. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,592
  11. 2023A Survey of Large Language Modelscited by 2,576
  12. 2022Emergent Abilities of Large Language Modelscited by 2,512
  13. 2019Fine-Tuning Language Models from Human Preferencescited by 2,150
  14. 2016Equality of Opportunity in Supervised Learningcited by 2,007
  15. 2022Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language modelscited by 1,998
  16. 2022Language Models (Mostly) Know What They Knowcited by 1,856
  17. 2022Large Language Models Encode Clinical Knowledgecited by 1,830
  18. 2019SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systemscited by 1,729
  19. 2023Evaluating Object Hallucination in Large Vision-Language Modelscited by 1,729
  20. 2023MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Modelscited by 1,652
  21. 2023Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generationcited by 1,391
  22. 2023FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generationcited by 1,282
  23. 2020Making Pre-trained Language Models Better Few-shot Learnerscited by 1,256
  24. 2020RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Modelscited by 1,255
  25. 2021BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Modelscited by 1,252
  26. 2022LaMDA: Language Models for Dialog Applicationscited by 1,220
  27. 2023HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Facecited by 1,190
  28. 2023Large Language Models are not Fair Evaluatorscited by 1,085
  29. 2021A General Language Assistant as a Laboratory for Alignmentcited by 1,055
  30. 2022Large Language Models Are Human-Level Prompt Engineerscited by 1,045
  31. 2020Beyond Accuracy: Behavioral Testing of NLP models with CheckListcited by 1,006
  32. 2021CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generationcited by 906
  33. 2021Measuring Coding Challenge Competence With APPScited by 902
  34. 2023A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivitycited by 894
  35. 2022CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesiscited by 885
  36. 2017Selective Classification for Deep Neural Networkscited by 852
  37. 2022GLM-130B: An Open Bilingual Pre-trained Modelcited by 843
  38. 2023Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedbackcited by 812

50 most cited of 180 All 180 (JSON)

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 107; 2024: 435; 2025: 477; 2026: 223.

1,242 papers.

Sort:

By area:

  1. 2023A Survey on Large Language Model based Autonomous Agentscited by 2,242
  2. 2024MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmarkcited by 1,848
  3. 2023Instruction-Following Evaluation for Large Language Modelscited by 1,228
  4. 2024OpenHands: An Open Platform for AI Software Developers as Generalist Agentscited by 1,002
  5. 2024Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMscited by 838
  6. 2023Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Modelscited by 698
  7. 2023Bias and Fairness in Large Language Models: A Surveycited by 639
  8. 2024A Survey on the Memory Mechanism of Large Language Model based Agentscited by 634
  9. 2023Prometheus: Inducing Fine-grained Evaluation Capability in Language Modelscited by 575
  10. 2024Large Language Models: A Surveycited by 525
  11. 2023LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Datasetcited by 461
  12. 2023Large Language Models for Software Engineering: A Systematic Literature Reviewcited by 399
38 more
  1. 2023A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Uglycited by 387
  2. 2023EvalCrafter: Benchmarking and Evaluating Large Video Generation Modelscited by 340
  3. 2024INSIDE: LLMs' Internal States Retain the Power of Hallucination Detectioncited by 323
  4. 2025From System 1 to System 2: A Survey of Reasoning Large Language Modelscited by 320
  5. 2023Benchmarking Large Language Models in Retrieval-Augmented Generationcited by 312
  6. 2023From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuningcited by 307
  7. 2023A Survey on Model Compression for Large Language Modelscited by 269
  8. 2024LiveBench: A Challenging, Contamination-Limited LLM Benchmarkcited by 262
  9. 2023Large Language Models Empowered Agent-based Modeling and Simulation: A Survey and Perspectivescited by 257
  10. 2023Challenges and Applications of Large Language Modelscited by 254
  11. 2023Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarizationcited by 246
  12. 2023Multilingual Jailbreak Challenges in Large Language Modelscited by 240
  13. 2023Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificitycited by 206
  14. 2024Judging the Judges: Evaluating Alignment and Vulnerabilities in LLMs-as-Judgescited by 205
  15. 2023Evaluating Large Language Models: A Comprehensive Surveycited by 202
  16. 2023FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Setscited by 201
  17. 2023Rethinking Benchmark and Contamination for Language Models with Rephrased Samplescited by 195
  18. 2023Efficient Large Language Models: A Surveycited by 186
  19. 2023Time Travel in LLMs: Tracing Data Contamination in Large Language Modelscited by 177
  20. 2023Can LLM-Generated Misinformation Be Detected?cited by 177
  21. 2024Secrets of RLHF in Large Language Models Part II: Reward Modelingcited by 171
  22. 2023ClassEval: A Manually-Crafted Benchmark for Evaluating LLMs on Class-level Code Generationcited by 168
  23. 2024Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Surveycited by 159
  24. 2023QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Modelscited by 155
  25. 2024Evaluation of Retrieval-Augmented Generation: A Surveycited by 146
  26. 2025LLM Post-Training: A Deep Dive into Reasoning Large Language Modelscited by 135
  27. 2025ELEPHANT: Measuring and understanding social sycophancy in LLMscited by 129
  28. 2024LlaSMol: Advancing Large Language Models for Chemistry with a Large-Scale, Comprehensive, High-Quality Instruction Tuning Datasetcited by 128
  29. 2024Mobile Edge Intelligence for Large Language Models: A Contemporary Surveycited by 128
  30. 2024Benchmarking LLMs via Uncertainty Quantificationcited by 126
  31. 2023A Survey of Large Language Models in Medicine: Progress, Application, and Challengecited by 124
  32. 2024AutoSurvey: Large Language Models Can Automatically Write Surveyscited by 119
  33. 2024A Practical Review of Mechanistic Interpretability for Transformer-Based Language Modelscited by 118
  34. 2023SciEval: A Multi-Level Large Language Model Evaluation Benchmark for Scientific Researchcited by 115
  35. 2024Is Mamba Effective for Time Series Forecasting?cited by 114
  36. 2024BetterBench: Assessing AI Benchmarks, Uncovering Issues, and Establishing Best Practicescited by 114
  37. 2024A Survey on Large Language Model-Based Game Agentscited by 112
  38. 2024Improved Techniques for Optimization-Based Jailbreaking on Large Language Modelscited by 110

50 most cited of 1,242 All 1,242 (JSON)