---
title: "Who cites CausalGym: Benchmarking — 34 citing papers"
description: "34 papers of the Elamur Research library cite «CausalGym: Benchmarking causal interpretability methods on linguistic tasks», found in their…"
canonical: "https://research.elamur.ai/free/en/papers/arora-2024-causalgym-benchmarking-causal-interpretability-methods/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/arora-2024-causalgym-benchmarking-causal-interpretability-methods/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [CausalGym: Benchmarking causal interpretability…](https://md.research.elamur.ai/free/en/papers/arora-2024-causalgym-benchmarking-causal-interpretability-methods/) / cited by

# Who cites «CausalGym: Benchmarking causal interpretability methods on linguistic tasks»

34 papers of our library cite it (of the 149,439 whose bibliographies we read) · 8 with a page here, the most cited first · [← back to «CausalGym: Benchmarking causal interpretability methods on linguistic tasks»](https://md.research.elamur.ai/free/en/papers/arora-2024-causalgym-benchmarking-causal-interpretability-methods/)

1. [**Mechanistic Interpretability for AI Safety — A Review arXiv:2404.14082**Leonard Bereska, Efstratios Gavves*2024 · 108 citations*](https://md.research.elamur.ai/free/en/papers/bereska-2024-mechanistic-interpretability-ai-safety-review/)
2. **[ReFT: Representation Finetuning for Language Models](https://md.research.elamur.ai/free/en/papers/wu-2024-reft-representation-finetuning-language-models/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zhengxuan Wu, Aryaman Arora, Zheng Wang, et al.*2024 · 74 citations*
3. [**A Primer on the Inner Workings of Transformer-based Language Models arXiv:2405.00208**Javier Ferrando, Gabriele Sarti, Arianna Bisazza, et al.*2024 · 72 citations*](https://md.research.elamur.ai/free/en/papers/ferrando-2024-primer-inner-workings-transformer-based/)
4. [**Causal Interventions Reveal Shared Structure Across English Filler–Gap Constructions**Sasha Boguraev, Christopher Potts, Kyle Mahowald*2025 · 13 citations*](https://md.research.elamur.ai/free/en/papers/boguraev-2025-causal-interventions-reveal-shared-structure/)
5. **[The Quest for the Right Mediator: Surveying Mechanistic Interpretability for NLP Through the Lens of Causal Mediation Analysis](https://md.research.elamur.ai/free/en/papers/mueller-2026-quest-right-mediator-surveying-mechanistic/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Aaron Mueller, Jannik Brinkmann, Millicent Li, et al.*2026 · 7 citations*
6. **[Mechanisms vs. Outcomes: Probing for Syntax Fails to Explain Performance on Targeted Syntactic Evaluations](https://md.research.elamur.ai/free/en/papers/agarwal-2025-mechanisms-vs-outcomes-probing-syntax/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ananth Agarwal, Jasper Jian, Christopher D Manning, et al.*2025 · 4 citations*
7. **[Mechanistic Interpretability Meets Cognitive Linguistics: Modelling Locative Image Schemas in the Circuit Framework](https://md.research.elamur.ai/free/en/papers/proietti-2026-mechanistic-interpretability-meets-cognitive-linguistics/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Mattia Proietti, Afra Alishahi, Grzegorz Chrupała, et al.*2026*
8. **[Memory efficiency and resource-rational encoding in sentence processing](https://md.research.elamur.ai/free/en/papers/xu-2026-memory-efficiency-resource-rational-encoding/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Weijie Xu, Brian Dillon, Richard Futrell*2026*

26 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/arora-2024-causalgym-benchmarking-causal-interpretability-methods/) — CausalGym: Benchmarking causal interpretability methods on linguistic tasks