---
title: "Who cites Information Flow Routes: — 37 citing papers"
description: "37 papers of the Elamur Research library cite «Information Flow Routes: Automatically Interpreting Language Models at Scale», found in their…"
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# Who cites «Information Flow Routes: Automatically Interpreting Language Models at Scale»

37 papers of our library cite it (of the 149,439 whose bibliographies we read) · 9 with a page here, the most cited first · [← back to «Information Flow Routes: Automatically Interpreting Language Models at Scale»](https://md.research.elamur.ai/free/en/papers/ferrando-2024-information-flow-routes-automatically-interpreting/)

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. [**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/)
3. **[Explainability and Interpretability of Multilingual Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/resck-2025-explainability-interpretability-multilingual-large-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Lucas Resck, Isabelle Augenstein, Anna Korhonen*2025 · 11 citations*
4. **[Unconditional Truthfulness: Learning Unconditional Uncertainty of Large Language Models](https://md.research.elamur.ai/free/en/papers/vazhentsev-2025-unconditional-truthfulness-learning-unconditional-uncertainty/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Artem Vazhentsev, Ekaterina Fadeeva, Rui Xing, et al.*2025 · 10 citations*
5. **[Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2026-locate-steer-improve-practical-survey/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hengyuan Zhang, Zhihao Zhang, Ercong Nie, et al.*2026 · 9 citations*
6. **[Diffusion-CAM: Faithful Visual Explanations for dMLLMs](https://md.research.elamur.ai/free/en/papers/zuo-2026-diffusion-cam-faithful-visual-explanations/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Haomin Zuo, Yidi Li, Luoxiao Yang, et al.*2026 · 1 citation*
7. **[Bridging Linguistic Structure and Mechanistic Interpretability for Conceptual Interpretation in Language Models](https://md.research.elamur.ai/free/en/papers/aljaafari-2026-bridging-linguistic-structure-mechanistic-interpretability/) [Conference on Computational Natural Language Learning (CoNLL)](https://md.research.elamur.ai/free/en/venues/conll/)**Nura Aljaafari, Danilo Carvalho, Andre Freitas*2026*
8. **[GKnow: Measuring the Entanglement of Gender Bias and Factual Gender](https://md.research.elamur.ai/free/en/papers/veloso-2026-gknow-measuring-entanglement-gender-bias/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Leonor Veloso, Hinrich Schuetze*2026*
9. **[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*

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

- [← back to](https://md.research.elamur.ai/free/en/papers/ferrando-2024-information-flow-routes-automatically-interpreting/) — Information Flow Routes: Automatically Interpreting Language Models at Scale