pith. sign in

arxiv: 2606.03695 · v1 · pith:5K7TH4TRnew · submitted 2026-06-02 · 💻 cs.CL

Don't Forget Your Embeddings: Robust Knowledge Erasure via Precise Editing of Embeddings

Clara Haya Suslik , Or Shafran , Mor Geva This is my paper

Pith reviewed 2026-06-28 10:35 UTC · model grok-4.3

classification 💻 cs.CL
keywords knowledge erasureconcept erasuretoken embeddingssparse matrix factorizationlanguage model safetyrelearning robustnessGemmaLlama
0
0 comments X

The pith

Precise editing of token embeddings is necessary for robust erasure of concepts from language models.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Existing knowledge erasure techniques in language models often allow the erased information to be recovered through prompting or relearning because they skip the embedding layer. The authors introduce EMBER, which uses sparse matrix factorization to precisely remove concept features from token embeddings before they enter the model. When added to prior methods, it boosts erasure performance on models like Gemma and Llama, cuts relearning success rates substantially, and keeps overall coherence largely intact. The analysis indicates that the side effects remain confined to a small number of tokens tied to the erased concept. This points to the embedding space as a critical but overlooked site for achieving lasting removal of unwanted knowledge.

Core claim

The paper establishes that precise embedding-level intervention is necessary for robust concept erasure. By augmenting existing parameter-update methods with a sparse matrix factorization module applied to token embeddings, erasure efficacy and specificity improve across task formats while coherence loss stays minimal. Robustness to relearning increases markedly, with regained accuracy dropping to as low as 35% on Llama-3.1-8B-Instruct compared to 70-76% without the embedding edit.

What carries the argument

EMBER, a plug-and-play erasure module that leverages Sparse Matrix Factorization to isolate and remove concept-related features from token embeddings.

If this is right

  • Augmenting existing methods with EMBER consistently improves erasure efficacy and specificity.
  • Relearning robustness improves, limiting regained accuracy to 35% on Llama versus 70-76% for prior methods.
  • Coherence cost is localized to a small set of concept-exclusive tokens.
  • Results hold across diverse concepts evaluated on Gemma-2-2B-it and Llama-3.1-8B-Instruct.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Embedding representations may encode concepts in a way that is more directly editable than internal parameters.
  • Relearning attacks might exploit retained embedding features even after higher-layer edits.
  • Similar sparse factorization approaches could be tested on other model components for enhanced erasure.

Load-bearing premise

The features associated with a concept are sparse enough and linearly separable enough in the embedding space that they can be factored out without broadly impacting other concepts or model behavior.

What would settle it

Observing that models augmented with EMBER still regain high accuracy on erased concepts after relearning attempts, or that coherence degrades across many unrelated tokens, would indicate the embedding intervention does not provide the claimed robustness.

Figures

Figures reproduced from arXiv: 2606.03695 by Clara Haya Suslik, Mor Geva, Or Shafran.

Figure 1
Figure 1. Figure 1: Existing concept erasure methods primarily [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Robustness evaluation results, showing for [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Distribution of TF-IDF scores (log scale) by [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Stage 1 prompt: the model describes the feature tokens without seeing the concept name. [PITH_FULL_IMAGE:figures/full_fig_p015_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Stage 2 prompt: given the Stage 1 description and the target concept name, the model classifies whether [PITH_FULL_IMAGE:figures/full_fig_p016_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Number of embedding features per concept surviving each ratio threshold [PITH_FULL_IMAGE:figures/full_fig_p017_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Mean number of LLM-labeled potential features per position, averaged over 18 concepts. The leftmost [PITH_FULL_IMAGE:figures/full_fig_p018_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Mean number of Gemma-2-2B-it MLP neu￾rons retained per layer under different coverage thresh￾olds γ, averaged over 18 concepts (dmlp = 9216). The grey bars show the WTA union (all non-zero neurons across concept features); coloured bars show the subset retained after the coverage filter. OE and MC question pairs from six concepts. Evaluation Splits We use the following valida￾tion/test splits: • Concept an… view at source ↗
Figure 9
Figure 9. Figure 9: Prompt template used to convert open-ended (OE) question–answer pairs into a four-option multiple [PITH_FULL_IMAGE:figures/full_fig_p019_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Example questions for three of the 18 con [PITH_FULL_IMAGE:figures/full_fig_p020_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Per-epoch concept accuracy during relearn [PITH_FULL_IMAGE:figures/full_fig_p021_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Post-erasure concept QA accuracy (Unlearn) [PITH_FULL_IMAGE:figures/full_fig_p022_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Post-erasure concept QA accuracy (Unlearn) [PITH_FULL_IMAGE:figures/full_fig_p024_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Correlation between µj and TF-IDF (log scale) pooled across all 18 concepts. Left: Gemma-2-2B-it. Right: Llama-3.1-8B-Instruct. Each point is one edited token; the regression line and 95% bootstrap CI band are overlaid. COVID COVID coronavirus pandemic corona pandemic Corona Pandemic lockdown SARS coronav lockdowns quarantine SARS virus CoV Omicron Virus omicron demics quarantined quarant onavir epidemic … view at source ↗
Figure 15
Figure 15. Figure 15: Per-token edit magnitude µj on a log scale (bar height and color both reflect µj ), with tokens ordered by descending µj (largest on the left). Left: COVID-19 Pandemic on Gemma-2-2B-it. Right: Harry Potter on Llama-3.1-8B-Instruct. ffindor _Rowling umbledore therin _Voldemort _Dumbledore _Hermione Hermione _Hogwarts demort _Weasley _Malfoy _Potter Harry _Harry Potter _Snape _Draco _Severus _Ginny _Beatles… view at source ↗
Figure 16
Figure 16. Figure 16: Per-token TF-IDF (bar height and color, log scale), with tokens ordered by descending edit magnitude [PITH_FULL_IMAGE:figures/full_fig_p026_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Prompt template used to elicit a concept-neutral context for each edited token. Placeholders [PITH_FULL_IMAGE:figures/full_fig_p029_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Prompt template used by the LLM judge to assign one of the three labels of § [PITH_FULL_IMAGE:figures/full_fig_p029_18.png] view at source ↗
read the original abstract

As language models are increasingly deployed in real-world applications, the ability to erase specific knowledge from them becomes critical for safety and compliance. Prominent methods seek persistent removal by updating the model's parameters, yet the target knowledge often can be recovered through adversarial prompting or relearning. In this work, we hypothesize this limitation stems in part from existing methods overlooking the embedding layer. To address this, we introduce EMBedding ERasure (EMBER), a plug-n-play erasure module that leverages Sparse Matrix Factorization for precise erasure of concept-related features from token embeddings. Through comprehensive evaluations across diverse concepts on Gemma-2-2B-it and Llama-3.1-8B-Instruct, we find that augmenting existing methods with EMBER consistently improves erasure efficacy and specificity across task formats, with minimal coherence loss. Moreover, it dramatically improves robustness to relearning, reducing regained accuracy by up to 50%, limiting it to 35% on Llama compared to 70%-76% for prior methods. Further analysis shows that the coherence cost is localized, affecting only a small set of concept-exclusive tokens. Our work establishes that precise embedding-level intervention is necessary for robust concept erasure, and demonstrates that existing methods can benefit from such augmentation.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 1 minor

Summary. The paper introduces EMBER, a plug-and-play module using Sparse Matrix Factorization (SMF) on token embeddings to erase concept-related features. It claims that augmenting existing parameter-update erasure methods with EMBER yields consistent gains in erasure efficacy and specificity across task formats on Gemma-2-2B-it and Llama-3.1-8B-Instruct, with minimal coherence loss that is localized to concept-exclusive tokens; crucially, it reports dramatically improved robustness to relearning (regained accuracy limited to 35% on Llama vs. 70-76% for prior methods) and concludes that precise embedding-level intervention is necessary for robust concept erasure.

Significance. If the empirical results hold after verification of the underlying assumptions, the work would be significant for AI safety and compliance applications by highlighting an overlooked component (the embedding layer) in knowledge erasure pipelines and showing that existing methods can be augmented for better persistence against relearning attacks.

major comments (3)
  1. [Method] The central claim that precise embedding-level intervention via SMF is necessary rests on the unverified assumption that concept-related features are sufficiently sparse and linearly separable in the token embedding space. No direct diagnostic is provided, such as the singular-value spectrum of the concept-token submatrix, overlap between learned factors and non-concept tokens, or ablation of factorization rank, to confirm this property holds for the evaluated concepts on Gemma-2 or Llama-3.1.
  2. [Experiments] The reported robustness gains (35% regained accuracy vs. 70-76% for baselines) and coherence results are presented without statistical tests, confidence intervals, or explicit controls for confounding factors such as prompt engineering variations or exact baseline re-implementations, undermining the strength of the cross-method comparison.
  3. [Analysis] The claim that coherence cost is localized (affecting only a small set of concept-exclusive tokens) is stated in the abstract and analysis but lacks quantitative support, such as the exact fraction or count of affected tokens and direct comparison against non-EMBER baselines.
minor comments (1)
  1. [Abstract] The abstract states 'reducing regained accuracy by up to 50%' but then specifies '35% on Llama'; aligning these figures with the exact baseline values and models would improve precision.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below and indicate the revisions we will make to the manuscript.

read point-by-point responses

  1. Referee: [Method] The central claim that precise embedding-level intervention via SMF is necessary rests on the unverified assumption that concept-related features are sufficiently sparse and linearly separable in the token embedding space. No direct diagnostic is provided, such as the singular-value spectrum of the concept-token submatrix, overlap between learned factors and non-concept tokens, or ablation of factorization rank, to confirm this property holds for the evaluated concepts on Gemma-2 or Llama-3.1.

    Authors: We acknowledge that the manuscript does not provide direct diagnostics such as singular-value spectra or factor overlap analysis to verify the sparsity and separability assumptions. While the consistent empirical gains across models support the approach, we agree these diagnostics would strengthen the central claim. In revision we will add the singular-value spectrum of the concept-token submatrix, quantify overlap between learned factors and non-concept tokens, and include an ablation on factorization rank for the evaluated concepts on Gemma-2-2B-it and Llama-3.1-8B-Instruct. revision: yes

  2. Referee: [Experiments] The reported robustness gains (35% regained accuracy vs. 70-76% for baselines) and coherence results are presented without statistical tests, confidence intervals, or explicit controls for confounding factors such as prompt engineering variations or exact baseline re-implementations, undermining the strength of the cross-method comparison.

    Authors: We agree that the results would be more robust with statistical validation. The revised manuscript will add statistical tests, confidence intervals over multiple runs, and explicit details on baseline re-implementations together with controls for prompt variations. revision: yes

  3. Referee: [Analysis] The claim that coherence cost is localized (affecting only a small set of concept-exclusive tokens) is stated in the abstract and analysis but lacks quantitative support, such as the exact fraction or count of affected tokens and direct comparison against non-EMBER baselines.

    Authors: We concur that the localization claim requires quantitative backing. We will revise the analysis section to report the exact fraction and count of affected tokens and provide direct comparisons against non-EMBER baselines. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical method proposal with independent experimental validation

full rationale

The paper introduces EMBER as a plug-in module using Sparse Matrix Factorization on token embeddings, then reports empirical gains in erasure robustness across Gemma-2 and Llama-3.1 models. No derivation chain, equations, or 'predictions' are present that reduce to fitted parameters or self-citations by construction. The central hypothesis (embedding layer overlooked by prior methods) is tested via augmentation experiments rather than assumed or redefined. Assumptions about sparsity/linear separability are unverified in the provided text but constitute an empirical premise, not a circular reduction. This matches the default case of a self-contained empirical study.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The method rests on standard linear-algebra assumptions about feature separability plus empirical choices for the factorization.

free parameters (1)
  • sparsity level / rank
    Tuned to isolate concept features while preserving coherence; value not stated in abstract.
axioms (1)
  • domain assumption Sparse matrix factorization isolates concept-specific directions in embedding space
    Invoked as the core mechanism of EMBER.

pith-pipeline@v0.9.1-grok · 5753 in / 1121 out tokens · 36773 ms · 2026-06-28T10:35:53.312859+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

64 extracted references · 10 canonical work pages

  1. [1]

    Precise In-Parameter Concept Erasure in Large Language Models

    Gur-Arieh, Yoav and Suslik, Clara Haya and Hong, Yihuai and Barez, Fazl and Geva, Mor. Precise In-Parameter Concept Erasure in Large Language Models. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025. doi:10.18653/v1/2025.emnlp-main.960

    work page doi:10.18653/v1/2025.emnlp-main.960 2025

  2. [2]

    Intrinsic Test of Unlearning Using Parametric Knowledge Traces

    Hong, Yihuai and Yu, Lei and Yang, Haiqin and Ravfogel, Shauli and Geva, Mor. Intrinsic Test of Unlearning Using Parametric Knowledge Traces. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025. doi:10.18653/v1/2025.emnlp-main.985

    work page doi:10.18653/v1/2025.emnlp-main.985 2025

  3. [3]

    Knowledge Neurons in Pretrained Transformers

    Dai, Damai and Dong, Li and Hao, Yaru and Sui, Zhifang and Chang, Baobao and Wei, Furu. Knowledge Neurons in Pretrained Transformers. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022. doi:10.18653/v1/2022.acl-long.581

    work page doi:10.18653/v1/2022.acl-long.581 2022

  4. [4]

    Linguistic Regularities in Continuous Space Word Representations

    Mikolov, Tomas and Yih, Wen-tau and Zweig, Geoffrey. Linguistic Regularities in Continuous Space Word Representations. Proceedings of the 2013 Conference of the North A merican Chapter of the Association for Computational Linguistics: Human Language Technologies. 2013

    2013

  5. [5]

    GloVe: Global vectors for word representation,

    Pennington, Jeffrey and Socher, Richard and Manning, Christopher. G lo V e: Global Vectors for Word Representation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing ( EMNLP ). 2014. doi:10.3115/v1/D14-1162

    work page doi:10.3115/v1/d14-1162 2014

  6. [6]

    Datasets: A Community Library for Natural Language Processing

    Lhoest, Quentin and Villanova del Moral, Albert and Jernite, Yacine and Thakur, Abhishek and von Platen, Patrick and Patil, Suraj and Chaumond, Julien and Drame, Mariama and Plu, Julien and Tunstall, Lewis and Davison, Joe and S a s ko, Mario and Chhablani, Gunjan and Malik, Bhavitvya and Brandeis, Simon and Le Scao, Teven and Sanh, Victor and Xu, Canwen ...

    work page doi:10.18653/v1/2021.emnlp-demo.21 2021

  7. [7]

    doi: 10.18653/v1/2020.emnlp-demos.6

    Wolf, Thomas and Debut, Lysandre and Sanh, Victor and Chaumond, Julien and Delangue, Clement and Moi, Anthony and Cistac, Pierric and Rault, Tim and Louf, Remi and Funtowicz, Morgan and Davison, Joe and Shleifer, Sam and von Platen, Patrick and Ma, Clara and Jernite, Yacine and Plu, Julien and Xu, Canwen and Le Scao, Teven and Gugger, Sylvain and Drame, M...

    work page doi:10.18653/v1/2020.emnlp-demos.6 2020

  8. [8]

    2026 , publisher =

    Ashuach, Tomer and Arad, Dana and Mueller, Aaron and Tutek, Martin and Belinkov, Yonatan , booktitle =. 2026 , publisher =

    2026

  9. [9]

    Proceedings of the 41st International Conference on Machine Learning , pages=

    The WMDP benchmark: measuring and reducing malicious use with unlearning , author=. Proceedings of the 41st International Conference on Machine Learning , pages=

  10. [10]

    Nature Machine Intelligence , volume=

    Rethinking machine unlearning for large language models , author=. Nature Machine Intelligence , volume=. 2025 , publisher=

    2025

  11. [11]

    Advances in Neural Information Processing Systems , volume=

    Large language model unlearning , author=. Advances in Neural Information Processing Systems , volume=

  12. [12]

    Findings of the Association for Computational Linguistics: ACL 2024 , pages=

    Towards safer large language models through machine unlearning , author=. Findings of the Association for Computational Linguistics: ACL 2024 , pages=

    2024

  13. [13]

    arXiv preprint arXiv:2410.08827 , year=

    Do unlearning methods remove information from language model weights? , author=. arXiv preprint arXiv:2410.08827 , year=

    arXiv

  14. [14]

    Advances in Neural Information Processing Systems , volume=

    Algorithmic capabilities of random transformers , author=. Advances in Neural Information Processing Systems , volume=

  15. [15]

    arXiv e-prints , pages=

    Catastrophic Failure of LLM Unlearning via Quantization , author=. arXiv e-prints , pages=

  16. [16]

    Artificial Intelligence Review , volume=

    Digital forgetting in large language models: A survey of unlearning methods , author=. Artificial Intelligence Review , volume=. 2025 , publisher=

    2025

  17. [17]

    First Conference on Language Modeling , year=

    Negative Preference Optimization: From Catastrophic Collapse to Effective Unlearning , author=. First Conference on Language Modeling , year=

  18. [18]

    2024 , eprint=

    Gemma 2: Improving Open Language Models at a Practical Size , author=. 2024 , eprint=

    2024

  19. [19]

    2024 , eprint=

    The Llama 3 Herd of Models , author=. 2024 , eprint=

    2024

  20. [20]

    9th International Conference on Learning Representations,

    Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt , title =. 9th International Conference on Learning Representations,. 2021 , publisher =

    2021

  21. [21]

    Hashimoto , title =

    Rohan Taori and Ishaan Gulrajani and Tianyi Zhang and Yann Dubois and Xuechen Li and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto , title =. GitHub repository , howpublished =. 2023 , publisher =

    2023

  22. [22]

    Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , pages=

    Dissecting recall of factual associations in auto-regressive language models , author=. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , pages=

    2023

  23. [23]

    Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing , pages=

    Minimal, Local, and Robust: Embedding-Only Edits for Implicit Bias in T2I Models , author=. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing , pages=

    2025

  24. [24]

    Advances in neural information processing systems , volume=

    Attention is all you need , author=. Advances in neural information processing systems , volume=

  25. [25]

    nature , volume=

    Learning the parts of objects by non-negative matrix factorization , author=. nature , volume=. 1999 , publisher=

    1999

  26. [26]

    Constructing Interpretable Features from Compositional Neuron Groups

    Shafran, Or and Geiger, Atticus and Geva, Mor. Constructing Interpretable Features from Compositional Neuron Groups. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026). 2026

    2026

  27. [27]

    Advances in Neural Information Processing Systems , volume=

    Refusal in language models is mediated by a single direction , author=. Advances in Neural Information Processing Systems , volume=

  28. [28]

    IEEE transactions on pattern analysis and machine intelligence , volume=

    Convex and Semi-Nonnegative Matrix Factorizations , author=. IEEE transactions on pattern analysis and machine intelligence , volume=

  29. [29]

    Advances in neural information processing systems , volume=

    Locating and editing factual associations in gpt , author=. Advances in neural information processing systems , volume=

  30. [30]

    The Eleventh International Conference on Learning Representations , year=

    Mass-Editing Memory in a Transformer , author=. The Eleventh International Conference on Learning Representations , year=

  31. [31]

    Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing , pages=

    Transformer feed-forward layers are key-value memories , author=. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing , pages=

    2021

  32. [32]

    2023 , journal=

    Towards Monosemanticity: Decomposing Language Models With Dictionary Learning , author=. 2023 , journal=

    2023

  33. [33]

    The Twelfth International Conference on Learning Representations , year=

    Sparse Autoencoders Find Highly Interpretable Features in Language Models , author=. The Twelfth International Conference on Learning Representations , year=

  34. [34]

    Advances in neural information processing systems , volume=

    Man is to computer programmer as woman is to homemaker? debiasing word embeddings , author=. Advances in neural information processing systems , volume=

  35. [35]

    Proceedings of the 58th annual meeting of the association for computational linguistics , pages=

    Null it out: Guarding protected attributes by iterative nullspace projection , author=. Proceedings of the 58th annual meeting of the association for computational linguistics , pages=

  36. [36]

    Proceedings 2025 Network and Distributed System Security Symposium , year=

    Safety Misalignment Against Large Language Models , author=. Proceedings 2025 Network and Distributed System Security Symposium , year=

    2025

  37. [37]

    arXiv preprint arXiv:2603.19302 , year=

    Parameter-Efficient Token Embedding Editing for Clinical Class-Level Unlearning , author=. arXiv preprint arXiv:2603.19302 , year=

    arXiv

  38. [38]

    ArXiv , year=

    LLM Unlearning Should Be Form-Independent , author=. ArXiv , year=

  39. [39]

    Chongyu Fan and Jinghan Jia and Yihua Zhang and Anil Ramakrishna and Mingyi Hong and Sijia Liu , booktitle=. Towards. 2025 , url=

    2025

  40. [40]

    Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

    Knowledge unlearning for mitigating privacy risks in language models , author=. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

  41. [41]

    ArXiv , year=

    Who's Harry Potter? Approximate Unlearning in LLMs , author=. ArXiv , year=

  42. [42]

    CoRR , volume=

    Aengus Lynch and Phillip Guo and Aidan Ewart and Stephen Casper and Dylan Hadfield-Menell , title=. CoRR , volume=. 2024 , cdate=

    2024

  43. [43]

    Transactions on Machine Learning Research , issn=

    Open Problems in Mechanistic Interpretability , author=. Transactions on Machine Learning Research , issn=. 2025 , url=

    2025

  44. [44]

    Hyperpolyglot

    Andrea W Wen-Yi and David Mimno , booktitle=. Hyperpolyglot. 2023 , url=

    2023

  45. [45]

    Advances in Neural Information Processing Systems , volume=

    Erasing conceptual knowledge from language models , author=. Advances in Neural Information Processing Systems , volume=

  46. [46]

    ArXiv , year=

    Word Meanings in Transformer Language Models , author=. ArXiv , year=

  47. [47]

    Black, and Otmar Hilliges

    Thomas Fel and Agustin Martin Picard and Louis B. 2023 , pages =. doi:10.1109/CVPR52729.2023.00266 , url =

    work page doi:10.1109/cvpr52729.2023.00266 2023

  48. [48]

    A Holistic Approach to Unifying Automatic Concept Extraction and Concept Importance Estimation , booktitle =

    Thomas Fel and Victor Boutin and Louis B. A Holistic Approach to Unifying Automatic Concept Extraction and Concept Importance Estimation , booktitle =. 2023 , editor =

    2023

  49. [49]

    Olshausen and Yann LeCun , title =

    Zeyu Yun and Yubei Chen and Bruno A. Olshausen and Yann LeCun , title =. Proceedings of Deep Learning Inside Out: The 2nd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures, DeeLIO@NAACL-HLT 2021, Online, June 10 2021 , year =. doi:10.18653/V1/2021.DEELIO-1.1 , url =

    work page doi:10.18653/v1/2021.deelio-1.1 2021

  50. [50]

    Deep Feature Factorization for Concept Discovery , booktitle =

    Edo Collins and Radhakrishna Achanta and Sabine S. Deep Feature Factorization for Concept Discovery , booktitle =. 2018 , pages =. doi:10.1007/978-3-030-01264-9\_21 , url =

    work page doi:10.1007/978-3-030-01264-9 2018

  51. [51]

    Scaling and evaluating sparse autoencoders , booktitle =

    Leo Gao and Tom Dupr. Scaling and evaluating sparse autoencoders , booktitle =. 2025 , publisher =

    2025

  52. [52]

    Frey , title =

    Alireza Makhzani and Brendan J. Frey , title =. 2nd International Conference on Learning Representations,. 2014 , editor =

    2014

  53. [53]

    Hoyer , title =

    Patrik O. Hoyer , title =. J. Mach. Learn. Res. , year =

  54. [54]

    and Rubinstein, Benjamin I

    Zhang, Ruihan and Madumal, Prashan and Miller, Tim and Ehinger, Krista A. and Rubinstein, Benjamin I. P. , title =. Proceedings of the AAAI Conference on Artificial Intelligence , year =. doi:10.1609/aaai.v35i13.17389 , url =

    work page doi:10.1609/aaai.v35i13.17389

  55. [55]

    ArXiv , year=

    Open Problems in Machine Unlearning for AI Safety , author=. ArXiv , year=

  56. [56]

    Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

    Enhancing automated interpretability with output-centric feature descriptions , author=. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

  57. [57]

    2023 , doi =

    Gemini: A Family of Highly Capable Multimodal Models , journal =. 2023 , doi =

    2023

  58. [58]

    2025 , eprint=

    Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities , author=. 2025 , eprint=

    2025

  59. [59]

    2026 , eprint=

    OpenAI GPT-5 System Card , author=. 2026 , eprint=

    2026

  60. [60]

    2026 , howpublished =

    2026

  61. [61]

    Wikimedia Downloads , year =

  62. [62]

    Gemini (Version 3.1 Flash Lite) , year =

  63. [63]

    Advances in Neural Information Processing Systems 32 , pages =

    PyTorch: An Imperative Style, High-Performance Deep Learning Library , author =. Advances in Neural Information Processing Systems 32 , pages =. 2019 , publisher =

    2019

  64. [64]

    2022 , howpublished =

    TransformerLens , author =. 2022 , howpublished =

    2022