mlsanityData to 14 Oct 2026

Mechanisms vs. Outcomes: Probing for Syntax Fails to Explain Performance on Targeted Syntactic Evaluations

arXiv 2506.16678 ↗First posted 20 Jun 2025cs.CLcited by 7

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Abstract (arXiv)

Large Language Models (LLMs) exhibit a robust mastery of syntax when processing and generating text. While this suggests internalized understanding of hierarchical syntax and dependency relations, the precise mechanism by which they represent syntactic structure is an open area within interpretability research. Probing provides one way to identify the mechanism of syntax being linearly encoded in activations, however, no comprehensive study has yet established whether a model's probing accuracy reliably predicts its downstream syntactic performance. Adopting a "mechanisms vs. outcomes" framework, we evaluate 32 open-weight transformer models and find that syntactic features extracted via probing fail to predict outcomes of targeted syntax evaluations across English linguistic phenomena. Our results highlight a substantial disconnect between latent syntactic representations found via probing and observable syntactic behaviors in downstream tasks.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 31 of its 55 references that are in the map. First posted on arXiv 20 Jun 2025.

Builds on

31 of 55 references.

References · 31

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  5. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
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  11. 2021Probing Classifiers: Promises, Shortcomings, and Advancescited by 876
  12. 2020Stanza: A Python Natural Language Processing Toolkit for Many Human Languagescited by 723
19 more
  1. 2019Designing and Interpreting Probes with Control Taskscited by 640
  2. 2024OLMo: Accelerating the Science of Language Modelscited by 613
  3. 2019BLiMP: The Benchmark of Linguistic Minimal Pairs for Englishcited by 564
  4. 2023The Falcon Series of Open Language Modelscited by 535
  5. 2018What you can cram into a single vector: Probing sentence embeddings for linguistic propertiescited by 457
  6. 2018Targeted Syntactic Evaluation of Language Modelscited by 242
  7. 2014SimLex-999: Evaluating Semantic Models with (Genuine) Similarity Estimationcited by 172
  8. 2020Probing the Probing Paradigm: Does Probing Accuracy Entail Task Relevance?cited by 151
  9. 2021Inducing Causal Structure for Interpretable Neural Networkscited by 110
  10. 2025MultiBLiMP 1.0: A Massively Multilingual Benchmark of Linguistic Minimal Pairscited by 71
  11. 2024CausalGym: Benchmarking causal interpretability methods on linguistic taskscited by 46
  12. 2022Characterizing Intrinsic Compositionality in Transformers with Tree Projectionscited by 41
  13. 2021What if This Modified That? Syntactic Interventions via Counterfactual Embeddingscited by 39
  14. 2022Probing for Incremental Parse States in Autoregressive Language Modelscited by 24
  15. 2024RuBLiMP: Russian Benchmark of Linguistic Minimal Pairscited by 24
  16. 2020Introducing Orthogonal Constraint in Structural Probescited by 21
  17. 2025TurBLiMP: A Turkish Benchmark of Linguistic Minimal Pairscited by 9
  18. 2023Exploring Linguistic Properties of Monolingual BERTs with Typological Classification among Languagescited by 6
  19. 2022Syntactic Substitutability as Unsupervised Dependency Syntaxcited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 7.

7 papers.

  1. 2026Tracing the complexity profiles of different linguistic phenomena through the intrinsic dimension of LLM representationscited by 3
  2. 2026The Grammar of Transformers: A Systematic Review of Interpretability Research on Syntactic Knowledge in Language Modelscited by 1
  3. 2026From Lemmas to Dependencies: What Signals Drive Light Verbs Classification?cited by 1
  4. 2026Linear representations of grammaticality in neural language modelscited by 1
  5. 2026Gated Tree Cross-Attention for Checkpoint-Compatible Syntax Injection in Decoder-Only LLMscited by 0
  6. 2026Child-directed speech facilitates production, not comprehension, in BabyLMscited by 0
  7. 2026Encoded but Not Decoded: Layer-Localized Evidence for a Three-Level Gap in LLM Syntaxcited by 0