--- title: "Interpretability — papers, page 2" description: "Interpretability: 47 papers of the Elamur Research library · page 2 of 2 · 26–47, the most cited first." canonical: "https://research.elamur.ai/free/en/topics/interpretability/page/2/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/topics/interpretability/page/2/) [home](https://md.research.elamur.ai/free/en/) / [topics](https://md.research.elamur.ai/free/en/topics/) / [Interpretability](https://md.research.elamur.ai/free/en/topics/interpretability/) / page 2 # 🔬 Interpretability 47 papers · page 2 of 2 · 26–47 · the most cited first · [all topics](https://md.research.elamur.ai/free/en/topics/) What neural models have learned inside, and how to show why they decide as they do. [← prev](https://md.research.elamur.ai/free/en/topics/interpretability/) [1](https://md.research.elamur.ai/free/en/topics/interpretability/) **2** 26. [**Scaling Laws and Interpretability of Learning from Repeated Data**Danny Hernandez, Tom Brown, Tom Conerly, et al.*2022 · 62 citations*](https://md.research.elamur.ai/free/en/papers/hernandez-2022-scaling-laws-interpretability-learning-repeated/) 27. [**Does Circuit Analysis Interpretability Scale? Evidence from Multiple Choice Capabilities in Chinchilla**Tom Lieberum, Matthew Rahtz, Janos Kramar, et al.*2023 · 51 citations*](https://md.research.elamur.ai/free/en/papers/lieberum-2023-circuit-analysis-interpretability-scale-evidence/) 28. [**Probing Contextual Language Models for Common Ground with Visual Representations**Gabriel Ilharco, Rowan Zellers, Ali Farhadi, et al.*2021 · 31 citations*](https://md.research.elamur.ai/free/en/papers/ilharco-2021-probing-contextual-language-models-common/) 29. [**When Your Language Model Cannot Even Do Determiners Right: Probing for Anti-Presuppositions and the Maximize Presupposition! Principle**Judith Sieker, Sina Zarrieß*2023 · 9 citations*](https://md.research.elamur.ai/free/en/papers/sieker-2023-when-your-language-model-cannot/) 30. [**Unveiling Language Competence Neurons: A Psycholinguistic Approach to Model Interpretability**Xufeng Duan, Xinyu Zhou, Bei Xiao, et al.*2025 · 8 citations*](https://md.research.elamur.ai/free/en/papers/duan-2025-unveiling-language-competence-neurons-psycholinguistic/) 31. [**Mechanisms vs. Outcomes: Probing for Syntax Fails to Explain Performance on Targeted Syntactic Evaluations**Ananth Agarwal, Jasper Jian, Christopher D Manning, et al.*2025 · 4 citations*](https://md.research.elamur.ai/free/en/papers/agarwal-2025-mechanisms-vs-outcomes-probing-syntax/) 32. [**Contextual Probing for Low-Resource Named Entity Recognition in Latin**Maria Mihaela Trusca, Mark Depauw, Violet Soen, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/trusca-2026-contextual-probing-low-resource-named/) 33. [**Probing for Reading Times**Eleftheria Tsipidi, Samuel Kiegeland, Francesco Ignazio Re, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/tsipidi-2026-probing-reading-times/) 34. [**Rhetorical Questions in LLM Representations: A Linear Probing Study**Louie Hong Yao, Vishesh Anand, Yuan Zhuang, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/yao-2026-rhetorical-questions-llm-representations-linear/) 35. [**Cross-Lingual Mental Health Ontologies for Indian Languages: Bridging Patient Expression and Clinical Understanding through Explainable AI and Human-in-the-Loop Validation**Ananth Kandala, Ratna Kandala, Akshata Kishore Moharir, et al.*2025 · 1 citation*](https://md.research.elamur.ai/free/en/papers/kandala-2025-cross-lingual-mental-health-ontologies/) 36. [**Bridging Linguistic Structure and Mechanistic Interpretability for Conceptual Interpretation in Language Models**Nura Aljaafari, Danilo Carvalho, Andre Freitas*2026*](https://md.research.elamur.ai/free/en/papers/aljaafari-2026-bridging-linguistic-structure-mechanistic-interpretability/) 37. [**Compositional Meaning Representations in LLMs: a Critical Review of Probing Studies**Rémy Marro*2026*](https://md.research.elamur.ai/free/en/papers/marro-2026-compositional-meaning-representations-llms-critical/) 38. [**Explainable AI for Ethical Counter Speech Generation in Hate Speech Mitigation**Ashiful Islam Ridoy, Mohammed Faisal, Yogesh Kumar, et al.*2026*](https://md.research.elamur.ai/free/en/papers/ridoy-2026-explainable-ai-ethical-counter-speech/) 39. [**From Insights to Impact: Actionable Interpretability for Neural Machine Translation**Gabriele Sarti*2026*](https://md.research.elamur.ai/free/en/papers/sarti-2026-insights-impact-actionable-interpretability-neural/) 40. [**Mechanistic Interpretability Meets Cognitive Linguistics: Modelling Locative Image Schemas in the Circuit Framework**Mattia Proietti, Afra Alishahi, Grzegorz Chrupała, et al.*2026*](https://md.research.elamur.ai/free/en/papers/proietti-2026-mechanistic-interpretability-meets-cognitive-linguistics/) 41. [**Probing Discrete Speech Tokens of Spoken Language Models**Sven Naber, Julia Koch, Pranav Singh, et al.*2026*](https://md.research.elamur.ai/free/en/papers/naber-2026-probing-discrete-speech-tokens-spoken/) 42. [**Probing the Attention Representation of Filler-Gap Dependency in Transformers**Ruoqing Yao, Pranav Anand*2026*](https://md.research.elamur.ai/free/en/papers/yao-2026-probing-attention-representation-filler-gap/) 43. [**Probing the Dynamics of Syntactic Ability Acquisition Throughout LLM Pretraining**Hiroshi Matsuda, Masayuki Asahara*2026*](https://md.research.elamur.ai/free/en/papers/matsuda-2026-probing-dynamics-syntactic-ability-acquisition/) 44. [**SAINT: Multilingual Span-Level Interpretability for Sentiment Analysis**Seid Muhie Yimam, Tadesse Destaw Belay, Robert Geislinger, et al.*2026*](https://md.research.elamur.ai/free/en/papers/yimam-2026-saint-multilingual-span-level-interpretability/) 45. [**Under the Surface: Probing Tamil Paraphrase Intelligence**Viswadarshan R R, Dr. J. Felicia Lilian, Mahalakshmi S*2026*](https://md.research.elamur.ai/free/en/papers/r-2026-under-surface-probing-tamil-paraphrase/) 46. [**Reverse Probing: Evaluating Knowledge Transfer via Finetuned Task Embeddings for Coreference Resolution**Tatiana Anikina, Arne Binder, David Harbecke, et al.*2025*](https://md.research.elamur.ai/free/en/papers/anikina-2025-reverse-probing-evaluating-knowledge-transfer/) 47. [**Qu'apporte BERT à l'analyse syntaxique en constituants discontinus ? Une suite de tests pour évaluer les prédictions de structures syntaxiques discontinues en anglais (What does BERT contribute to discontinuous constituency parsing ? A test suite to evaluate discontinuous constituency structure predictions in English)**Maximin Coavoux*2020*](https://md.research.elamur.ai/free/en/papers/coavoux-2020-qu-apporte-bert-l-analyse/) [← prev](https://md.research.elamur.ai/free/en/topics/interpretability/) [1](https://md.research.elamur.ai/free/en/topics/interpretability/) **2**