mlsanityData to 14 Oct 2026

An Evaluation on Large Language Model Outputs: Discourse and Memorization

arXiv 2304.08637 ↗First posted 17 Apr 2023cs.CL cs.AI cs.LGcited by 20

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Abstract (arXiv)

We present an empirical evaluation of various outputs generated by nine of the most widely-available large language models (LLMs). Our analysis is done with off-the-shelf, readily-available tools. We find a correlation between percentage of memorized text, percentage of unique text, and overall output quality, when measured with respect to output pathologies such as counterfactual and logically-flawed statements, and general failures like not staying on topic. Overall, 80.0% of the outputs evaluated contained memorized data, but outputs containing the most memorized content were also more likely to be considered of high quality. We discuss and evaluate mitigation strategies, showing that, in the models evaluated, the rate of memorized text being output is reduced. We conclude with a discussion on potential implications around what it means to learn, to memorize, and to evaluate quality text.

Key facts. Cited by 20 papers in the map (data through 14 Oct 2026). Builds on 42 of its 59 references that are in the map. First posted on arXiv 17 Apr 2023.

Builds on

42 of 59 references.

References · 42

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2023GPT-4 Technical Reportcited by 18,396
  3. 2022Training language models to follow instructions with human feedbackcited by 15,971
  4. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  5. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  6. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  7. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,326
  8. 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,512
  9. 2019PIQA: Reasoning about Physical Commonsense in Natural Languagecited by 2,883
  10. 2020The Pile: An 800GB Dataset of Diverse Text for Language Modelingcited by 2,411
  11. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,234
  12. 2020Extracting Training Data from Large Language Modelscited by 2,189
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  1. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,141
  2. 2022BLOOM: A 176B-Parameter Open-Access Multilingual Language Modelcited by 1,984
  3. 2019Language Models as Knowledge Bases?cited by 1,936
  4. 2022Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Themcited by 1,807
  5. 2021Improving language models by retrieving from trillions of tokenscited by 1,293
  6. 2020RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Modelscited by 1,255
  7. 2022Large Language Models Are Human-Level Prompt Engineerscited by 1,045
  8. 2019How Can We Know What Language Models Know?cited by 959
  9. 2018The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networkscited by 864
  10. 2022Quantifying Memorization Across Neural Language Modelscited by 763
  11. 2022Galactica: A Large Language Model for Sciencecited by 744
  12. 2021Deduplicating Training Data Makes Language Models Bettercited by 714
  13. 2022GPT-NeoX-20B: An Open-Source Autoregressive Language Modelcited by 713
  14. 2019The Woman Worked as a Babysitter: On Biases in Language Generationcited by 541
  15. 2020What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimationcited by 497
  16. 2019Does Learning Require Memorization? A Short Tale about a Long Tailcited by 460
  17. 2023Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedbackcited by 353
  18. 2022Deduplicating Training Data Mitigates Privacy Risks in Language Modelscited by 314
  19. 2020The Pushshift Reddit Datasetcited by 292
  20. 2019Measuring Bias in Contextualized Word Representationscited by 276
  21. 2020COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphscited by 254
  22. 2022OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalizationcited by 236
  23. 2020How Context Affects Language Models' Factual Predictionscited by 181
  24. 2021The Curious Case of Hallucinations in Neural Machine Translationcited by 161
  25. 2020The Radicalization Risks of GPT-3 and Advanced Neural Language Modelscited by 76
  26. 2022Do Language Models Plagiarize?cited by 73
  27. 2022Contrastive Search Is What You Need For Neural Text Generationcited by 59
  28. 2022Characteristics of Harmful Text: Towards Rigorous Benchmarking of Language Modelscited by 49
  29. 2021Responsible AI: Gender bias assessment in emotion recognitioncited by 22
  30. 2022Finding Memo: Extractive Memorization in Constrained Sequence Generation Taskscited by 12

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Citing papers per year: 2023: 6; 2024: 7; 2025: 5; 2026: 2.

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