# Structural Persistence in Language Models: Priming as a Window into Abstract Language Representations

arXiv: https://arxiv.org/abs/2109.14989 · HTML: https://mlsanity.com/paper/2109.14989/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- Builds on 40 of its 103 references that are in the map.
- First posted on arXiv 30 Sep 2021.

## Builds on (most cited first, 30 of 40)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [BERT Rediscovers the Classical NLP Pipeline](https://mlsanity.com/paper/1905.05950/) (2019)
- [Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference](https://mlsanity.com/paper/1902.01007/) (2019)
- [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://mlsanity.com/paper/1911.00536/) (2019)
- [A Primer in BERTology: What we know about how BERT works](https://mlsanity.com/paper/2002.12327/) (2020)
- [BLiMP: The Benchmark of Linguistic Minimal Pairs for English](https://mlsanity.com/paper/1912.00582/) (2019)
- [What do you learn from context? Probing for sentence structure in contextualized word representations](https://mlsanity.com/paper/1905.06316/) (2019)
- [Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies](https://mlsanity.com/paper/1611.01368/) (2016)
- [What BERT is not: Lessons from a new suite of psycholinguistic diagnostics for language models](https://mlsanity.com/paper/1907.13528/) (2019)
- [Negated and Misprimed Probes for Pretrained Language Models: Birds Can Talk, But Cannot Fly](https://mlsanity.com/paper/1911.03343/) (2019)
- [Information-Theoretic Probing with Minimum Description Length](https://mlsanity.com/paper/2003.12298/) (2020)
- [Colorless green recurrent networks dream hierarchically](https://mlsanity.com/paper/1803.11138/) (2018)
- [Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little](https://mlsanity.com/paper/2104.06644/) (2021)
- [Exploring the Limits of Language Modeling](https://mlsanity.com/paper/1602.02410/) (2016)
- [A Systematic Assessment of Syntactic Generalization in Neural Language Models](https://mlsanity.com/paper/2005.03692/) (2020)
- [Neural Language Models as Psycholinguistic Subjects: Representations of Syntactic State](https://mlsanity.com/paper/1903.03260/) (2019)
- [The emergence of number and syntax units in LSTM language models](https://mlsanity.com/paper/1903.07435/) (2019)
- [A Theoretical Analysis of the Repetition Problem in Text Generation](https://mlsanity.com/paper/2012.14660/) (2020)
- [Out of Order: How Important Is The Sequential Order of Words in a Sentence in Natural Language Understanding Tasks?](https://mlsanity.com/paper/2012.15180/) (2020)
- [UnNatural Language Inference](https://mlsanity.com/paper/2101.00010/) (2020)
- [Investigating BERT's Knowledge of Language: Five Analysis Methods with NPIs](https://mlsanity.com/paper/1909.02597/) (2019)
- [Distributional Semantics and Linguistic Theory](https://mlsanity.com/paper/1905.01896/) (2019)
- [On the proper role of linguistically-oriented deep net analysis in linguistic theorizing](https://mlsanity.com/paper/2106.08694/) (2021)
- [Mechanisms for Handling Nested Dependencies in Neural-Network Language Models and Humans](https://mlsanity.com/paper/2006.11098/) (2020)
- [Refining Targeted Syntactic Evaluation of Language Models](https://mlsanity.com/paper/2104.09635/) (2021)

## Built on by (most cited first, 30 of 44)

- [Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection](https://mlsanity.com/paper/2302.12173/) (2023)
- [Do LLMs exhibit human-like response biases? A case study in survey design](https://mlsanity.com/paper/2311.04076/) (2023)
- [Language Model Behavior: A Comprehensive Survey](https://mlsanity.com/paper/2303.11504/) (2023)
- [Do Large Language Models know what humans know?](https://mlsanity.com/paper/2209.01515/) (2022)
- [Modeling rapid language learning by distilling Bayesian priors into artificial neural networks](https://mlsanity.com/paper/2305.14701/) (2023)
- [Can language models handle recursively nested grammatical structures? A case study on comparing models and humans](https://mlsanity.com/paper/2210.15303/) (2022)
- [The broader spectrum of in-context learning](https://mlsanity.com/paper/2412.03782/) (2024)
- [Do large language models resemble humans in language use?](https://mlsanity.com/paper/2303.08014/) (2023)
- [A Better Way to Do Masked Language Model Scoring](https://mlsanity.com/paper/2305.10588/) (2023)
- [Information Value: Measuring Utterance Predictability as Distance from Plausible Alternatives](https://mlsanity.com/paper/2310.13676/) (2023)
- [Language model acceptability judgements are not always robust to context](https://mlsanity.com/paper/2212.08979/) (2022)
- [Machine Psychology](https://mlsanity.com/paper/2303.13988/) (2023)
- [Do Language Models Exhibit Human-like Structural Priming Effects?](https://mlsanity.com/paper/2406.04847/) (2024)
- [Evaluating Neural Language Models as Cognitive Models of Language Acquisition](https://mlsanity.com/paper/2310.20093/) (2023)
- [Herd Behavior: Investigating Peer Influence in LLM-based Multi-Agent Systems](https://mlsanity.com/paper/2505.21588/) (2025)
- [When Language Models Fall in Love: Animacy Processing in Transformer Language Models](https://mlsanity.com/paper/2310.15004/) (2023)
- [Using Artificial Populations to Study Psychological Phenomena in Neural Models](https://mlsanity.com/paper/2308.08032/) (2023)
- [Large Language Model Recall Uncertainty is Modulated by the Fan Effect](https://mlsanity.com/paper/2407.06349/) (2024)
- [Do language models accommodate their users? A study of linguistic convergence](https://mlsanity.com/paper/2508.03276/) (2025)
- [Attribution and Alignment: Effects of Local Context Repetition on Utterance Production and Comprehension in Dialogue](https://mlsanity.com/paper/2311.13061/) (2023)
- [Interpretability of Language Models via Task Spaces](https://mlsanity.com/paper/2406.06441/) (2024)
- [Is In-Context Learning a Type of Error-Driven Learning? Evidence from the Inverse Frequency Effect in Structural Priming](https://mlsanity.com/paper/2406.18501/) (2024)
- [Large Language Models Are Partially Primed in Pronoun Interpretation](https://mlsanity.com/paper/2305.16917/) (2023)
- [Experimental Contexts Can Facilitate Robust Semantic Property Inference in Language Models, but Inconsistently](https://mlsanity.com/paper/2401.06640/) (2024)
- [SPAWNing Structural Priming Predictions from a Cognitively Motivated Parser](https://mlsanity.com/paper/2403.07202/) (2024)
- [Modeling Bilingual Sentence Processing: Evaluating RNN and Transformer Architectures for Cross-Language Structural Priming](https://mlsanity.com/paper/2405.09508/) (2024)
- [LAMMI-Pathology: A Tool-Centric Bottom-Up LVLM-Agent Framework for Molecularly Informed Medical Intelligence in Pathology](https://mlsanity.com/paper/2602.18773/) (2026)
- [Structural Ambiguity and its Disambiguation in Language Model Based Parsers: the Case of Dutch Clause Relativization](https://mlsanity.com/paper/2305.14917/) (2023)
- [The Grammar of Transformers: A Systematic Review of Interpretability Research on Syntactic Knowledge in Language Models](https://mlsanity.com/paper/2601.19926/) (2026)
- [In-Context Learning in Speech Language Models: Analyzing the Role of Acoustic Features, Linguistic Structure, and Induction Heads](https://mlsanity.com/paper/2604.06356/) (2026)

## Abstract (arXiv)

We investigate the extent to which modern, neural language models are susceptible to structural priming, the phenomenon whereby the structure of a sentence makes the same structure more probable in a follow-up sentence. We explore how priming can be used to study the potential of these models to learn abstract structural information, which is a prerequisite for good performance on tasks that require natural language understanding skills. We introduce a novel metric and release Prime-LM, a large corpus where we control for various linguistic factors which interact with priming strength. We find that Transformer models indeed show evidence of structural priming, but also that the generalisations they learned are to some extent modulated by semantic information. Our experiments also show that the representations acquired by the models may not only encode abstract sequential structure but involve certain level of hierarchical syntactic information. More generally, our study shows that the priming paradigm is a useful, additional tool for gaining insights into the capacities of language models and opens the door to future priming-based investigations that probe the model's internal states.