# Low-Perplexity LLM-Generated Sequences and Where To Find Them

arXiv: https://arxiv.org/abs/2507.01844 · HTML: https://mlsanity.com/paper/2507.01844/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 28 references that are in the map.
- First posted on arXiv 2 Jul 2025.

## Builds on (most cited first, 21 of 21)

- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://mlsanity.com/paper/2101.00027/) (2020)
- [Extracting Training Data from Large Language Models](https://mlsanity.com/paper/2012.07805/) (2020)
- [Generalization through Memorization: Nearest Neighbor Language Models](https://mlsanity.com/paper/1911.00172/) (2019)
- [Extracting Training Data from Diffusion Models](https://mlsanity.com/paper/2301.13188/) (2023)
- [What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation](https://mlsanity.com/paper/2008.03703/) (2020)
- [Representation Degeneration Problem in Training Natural Language Generation Models](https://mlsanity.com/paper/1907.12009/) (2019)
- [Demystifying Prompts in Language Models via Perplexity Estimation](https://mlsanity.com/paper/2212.04037/) (2022)
- [What Does it Mean for a Language Model to Preserve Privacy?](https://mlsanity.com/paper/2202.05520/) (2022)
- [Infini-gram: Scaling Unbounded n-gram Language Models to a Trillion Tokens](https://mlsanity.com/paper/2401.17377/) (2024)
- [How much do language models copy from their training data? Evaluating linguistic novelty in text generation using RAVEN](https://mlsanity.com/paper/2111.09509/) (2021)
- [Mapping the Increasing Use of LLMs in Scientific Papers](https://mlsanity.com/paper/2404.01268/) (2024)
- [Simfluence: Modeling the Influence of Individual Training Examples by Simulating Training Runs](https://mlsanity.com/paper/2303.08114/) (2023)
- [Scalable Influence and Fact Tracing for Large Language Model Pretraining](https://mlsanity.com/paper/2410.17413/) (2024)
- [Recite, Reconstruct, Recollect: Memorization in LMs as a Multifaceted Phenomenon](https://mlsanity.com/paper/2406.17746/) (2024)
- [Traces of Memorisation in Large Language Models for Code](https://mlsanity.com/paper/2312.11658/) (2023)
- [Evaluating n-Gram Novelty of Language Models Using Rusty-DAWG](https://mlsanity.com/paper/2406.13069/) (2024)
- [Detecting and Filtering Unsafe Training Data via Data Attribution with Denoised Representation](https://mlsanity.com/paper/2502.11411/) (2025)
- [Enhancing Training Data Attribution for Large Language Models with Fitting Error Consideration](https://mlsanity.com/paper/2410.01285/) (2024)
- [Training Data Attribution (TDA): Examining Its Adoption & Use Cases](https://mlsanity.com/paper/2501.12642/) (2025)
- [SoftMatcha: A Soft and Fast Pattern Matcher for Billion-Scale Corpus Searches](https://mlsanity.com/paper/2503.03703/) (2025)

## Built on by (most cited first, 1 of 1)

- [Output Vector Editing for Memorization Mitigation in Large Language Models](https://mlsanity.com/paper/2606.18767/) (2026)

## Abstract (arXiv)

As Large Language Models (LLMs) become increasingly widespread, understanding how specific training data shapes their outputs is crucial for transparency, accountability, privacy, and fairness. To explore how LLMs leverage and replicate their training data, we introduce a systematic approach centered on analyzing low-perplexity sequences - high-probability text spans generated by the model. Our pipeline reliably extracts such long sequences across diverse topics while avoiding degeneration, then traces them back to their sources in the training data. Surprisingly, we find that a substantial portion of these low-perplexity spans cannot be mapped to the corpus. For those that do match, we quantify the distribution of occurrences across source documents, highlighting the scope and nature of verbatim recall and paving a way toward better understanding of how LLMs training data impacts their behavior.