arxiv: 2604.21300 · v1 · submitted 2026-04-23 · 💻 cs.CL · cs.IR· cs.LG

Recognition: unknown

Explainable Disentangled Representation Learning for Generalizable Authorship Attribution in the Era of Generative AI

Hieu Man , Van-Cuong Pham , Nghia Trung Ngo , Franck Dernoncourt , Thien Huu Nguyen

Authors on Pith no claims yet

Pith reviewed 2026-05-09 21:26 UTC · model grok-4.3

classification 💻 cs.CL cs.IRcs.LG

keywords authorship attributiondisentangled representationsvariational autoencoderAI-generated text detectionexplainable AIstyle-content disentanglementcontrastive learninggenerative AI

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The pith

A variational autoencoder with separate style and content encoders plus an explanatory discriminator yields more generalizable authorship attribution and AI-text detection.

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

The paper sets out to show that current models for figuring out who wrote a text fail when authors switch topics because they entangle style with content. It introduces EAVAE, which first pretrains a style encoder with contrastive learning, then uses a variational autoencoder with dedicated encoders for style and content, and trains a discriminator that both decides whether representations match the same author or source and writes a short natural-language justification for that decision. The goal is representations that stay stable across domains. If this works, attribution systems would remain reliable even when the same writer covers new subjects, and few-shot detection of machine-generated text would improve without needing large labeled sets for every new generator.

Core claim

EAVAE achieves disentangled style representations by architectural separation of encoders in a variational autoencoder, after supervised contrastive pretraining on authorship data, and by training a discriminator that distinguishes same-author or same-content pairs while also generating natural language explanations of its distinctions. These representations deliver state-of-the-art authorship attribution accuracy on Amazon Reviews, PAN21, and HRS datasets and strong few-shot performance on the M4 dataset for AI-generated text detection.

What carries the argument

The Explainable Authorship Variational Autoencoder (EAVAE) that uses separate encoders for style and content together with a discriminator that both classifies representation pairs and produces natural-language explanations for its classifications.

If this is right

Authorship attribution reaches state-of-the-art accuracy on Amazon Reviews, PAN21, and HRS benchmarks.
Few-shot detection of AI-generated text outperforms prior methods on the M4 dataset.
Representations generalize better across domains because content-style correlations are reduced by design.
Natural-language explanations are produced alongside every discrimination decision, increasing model transparency.

Where Pith is reading between the lines

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

The same separation technique could be tested on other tasks where style and topic mix, such as detecting coordinated inauthentic behavior across platforms.
If the generated explanations align with human judgments on style features, they could serve as a diagnostic tool for spotting when a model is relying on topic shortcuts.
Extending the pretraining stage to include more diverse authorship sources might further reduce domain shift without additional labeled target data.

Load-bearing premise

The discriminator's classification and explanation steps truly remove correlations between style and content rather than learning new spurious patterns that work on the chosen test sets.

What would settle it

Evaluate the model on texts written by the same authors but on topics completely absent from training data; if attribution accuracy falls close to random guessing while explanation quality remains high, the disentanglement claim is unsupported.

Figures

Figures reproduced from arXiv: 2604.21300 by Franck Dernoncourt, Hieu Man, Nghia Trung Ngo, Thien Huu Nguyen, Van-Cuong Pham.

**Figure 2.** Figure 2: The architecture of Explainable Authorship [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Figure 3: The architecture of unified generator with [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

read the original abstract

Learning robust representations of authorial style is crucial for authorship attribution and AI-generated text detection. However, existing methods often struggle with content-style entanglement, where models learn spurious correlations between authors' writing styles and topics, leading to poor generalization across domains. To address this challenge, we propose Explainable Authorship Variational Autoencoder (EAVAE), a novel framework that explicitly disentangles style from content through architectural separation-by-design. EAVAE first pretrains style encoders using supervised contrastive learning on diverse authorship data, then finetunes with a Variational Autoencoder (VEA) architecture using separate encoders for style and content representations. Disentanglement is enforced through a novel discriminator that not only distinguishes whether pairs of style/content representations belong to the same or different authors/content sources, but also generates natural language explanation for their decision, simultaneously mitigating confounding information and enhancing interpretability. Extensive experiments demonstrate the effectiveness of EAVAE. On authorship attribution, we achieve state-of-the-art performance on various datasets, including Amazon Reviews, PAN21, and HRS. For AI-generated text detection, EAVAE excels in few-shot learning over the M4 dataset. Code and data repositories are available online\footnote{https://github.com/hieum98/avae} \footnote{https://huggingface.co/collections/Hieuman/document-level-authorship-datasets}.

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.

Desk Editor's Note private letter to a colleague

EAVAE adds contrastive pretraining and an explainable discriminator to a style-content VAE, but the abstract gives no numbers or checks to support the SOTA claims.

read the letter

The main takeaway is a new pipeline for authorship attribution that pretrains style encoders with supervised contrastive learning, then uses a VAE with separate style and content encoders plus a discriminator that both classifies pairs and outputs natural-language explanations for its decisions. That discriminator is the clearest addition over standard disentanglement setups in this area. The code and dataset releases on GitHub and Hugging Face are also practical pluses for anyone who wants to try the method directly. The problem itself matters in the current generative-AI setting, where style-content entanglement hurts generalization on real-world data like reviews or news.

Referee Report

2 major / 1 minor

Summary. The paper introduces the Explainable Authorship Variational Autoencoder (EAVAE), which pretrains style encoders via supervised contrastive learning on authorship data and then finetunes a VAE using separate encoders for style and content representations. A novel discriminator classifies whether pairs of style/content vectors belong to the same or different authors/sources while also generating natural-language explanations for its decisions. The authors claim this architectural separation plus discriminator achieves effective disentanglement, yielding state-of-the-art authorship attribution on Amazon Reviews, PAN21, and HRS, plus strong few-shot performance on AI-generated text detection over the M4 dataset.

Significance. If the claimed disentanglement is verified as removing rather than merely reconfiguring style-content correlations and if the generated explanations are shown to be faithful to the learned representations, the framework could meaningfully advance generalizable and interpretable authorship attribution in the presence of generative AI. The public release of code and datasets is a concrete strength supporting reproducibility.

major comments (2)

[Abstract and §3] Abstract and §3 (method description): the central claim that the discriminator 'simultaneously mitigating confounding information' rests on the unverified assumption that its natural-language explanations are faithful to the representations rather than post-hoc; no mention is made of a faithfulness objective, ablation of the explanation head, or representation-level probes (mutual information, linear probes for residual content in style vectors).
[§5] §5 (experiments): while SOTA results are asserted on Amazon Reviews, PAN21, HRS, and M4 few-shot, the absence of reported ablations isolating the discriminator's contribution, quantitative disentanglement metrics (e.g., MIG, SAP), and error analysis on domain-shift cases leaves the generalization claim load-bearing but unsupported in the provided evaluation.

minor comments (1)

[Abstract] The two footnote URLs for code and data should be consolidated into a single, persistent repository link with explicit commit hashes or version tags.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback. We address each major comment point by point below, agreeing where additional verification and analyses are needed to strengthen the claims, and outlining the specific revisions we will incorporate.

read point-by-point responses

Referee: [Abstract and §3] Abstract and §3 (method description): the central claim that the discriminator 'simultaneously mitigating confounding information' rests on the unverified assumption that its natural-language explanations are faithful to the representations rather than post-hoc; no mention is made of a faithfulness objective, ablation of the explanation head, or representation-level probes (mutual information, linear probes for residual content in style vectors).

Authors: We agree that the manuscript does not explicitly verify the faithfulness of the discriminator's natural-language explanations or include dedicated checks for residual confounding. The discriminator is trained jointly to perform classification and generate explanations, with the intent that this encourages disentanglement beyond post-hoc rationalization, but we acknowledge the lack of supporting analyses. In the revised version, we will add an ablation removing the explanation head to quantify its contribution, along with representation-level probes: mutual information estimation between style and content vectors, and linear probes trained to predict content attributes from style vectors. These will be reported in §3 and §5 to directly address the assumption and confirm mitigation of confounding information. revision: yes
Referee: [§5] §5 (experiments): while SOTA results are asserted on Amazon Reviews, PAN21, HRS, and M4 few-shot, the absence of reported ablations isolating the discriminator's contribution, quantitative disentanglement metrics (e.g., MIG, SAP), and error analysis on domain-shift cases leaves the generalization claim load-bearing but unsupported in the provided evaluation.

Authors: We concur that the current evaluation, while showing strong SOTA results, would be more convincing with explicit isolation of components and quantitative support for disentanglement and generalization. We will revise §5 to include: (i) ablations comparing the full model against a variant without the discriminator to isolate its contribution; (ii) standard quantitative disentanglement metrics including Mutual Information Gap (MIG) and Separated Attribute Predictability (SAP) computed on the learned style and content representations; and (iii) a dedicated error analysis on domain-shift cases, with performance breakdowns by topic shifts and cross-domain author attribution. These additions will provide direct evidence for the generalization claims. revision: yes

Circularity Check

0 steps flagged

No circularity: standard contrastive+VAE objectives applied to new architecture with no self-referential equations or load-bearing self-citations

full rationale

The abstract and method description introduce EAVAE via pretraining with supervised contrastive learning on authorship data followed by VAE finetuning with separate style/content encoders and a discriminator that classifies pairs while generating explanations. No equations appear that define a target quantity in terms of itself, rename a fitted parameter as a prediction, or reduce the central claim to a self-citation chain. The discriminator and disentanglement are presented as architectural choices whose effectiveness is asserted via downstream experiments rather than derived tautologically from the inputs. This matches the default case of a self-contained empirical method whose performance claims rest on external benchmarks, not internal reduction.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 1 invented entities

Review performed on abstract only; the method implicitly relies on standard VAE evidence lower-bound assumptions and contrastive loss formulations whose hyperparameters are not detailed.

free parameters (1)

latent dimension and beta weighting in VAE
Typical free parameters in any VAE implementation whose values are chosen to balance reconstruction and regularization but not reported here.

axioms (1)

standard math The variational autoencoder evidence lower bound provides a valid training objective for disentangled representations when encoders are separated by design.
Invoked by the choice of VAE architecture for style-content separation.

invented entities (1)

Explainable discriminator that outputs natural-language decisions no independent evidence
purpose: Enforce disentanglement while producing human-readable explanations for style/content pair judgments.
New component introduced to mitigate confounding and add interpretability; no independent evidence of explanation fidelity provided in abstract.

pith-pipeline@v0.9.0 · 5567 in / 1408 out tokens · 51307 ms · 2026-05-09T21:26:01.641789+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

12 extracted references · 7 canonical work pages · 1 internal anchor

[1]

Whodunit? learning to contrast for authorship attribution. InProceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 1142–1157, Online only. Association for Computational Linguistics. Malik Altakr...

work page arXiv 2021
[2]

Domain-Adversarial Training of Neural Networks

Domain-adversarial training of neural net- works.Preprint, arXiv:1505.07818. Pengzhi Gao, Liwen Zhang, Zhongjun He, Hua Wu, and Haifeng Wang. 2023. Learning multilingual sentence representations with cross-lingual consistency regu- larization. InProceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 2...

work page Pith review arXiv 2023
[3]

Dense passage retrieval for open-domain question answering

Supervised contrastive learning.Preprint, arXiv:2004.11362. Diederik P Kingma and Max Welling. 2022. Auto-encoding variational bayes.Preprint, arXiv:1312.6114. Varada Kolhatkar, Hanhan Wu, Luca Cavasso, Emilie Francis, Kavan Shukla, and Maite Taboada. 2020. The sfu opinion and comments corpus: A corpus for the analysis of online news comments.Corpus pragm...

work page arXiv 2004
[4]

Generative representational instruction tuning.arXiv preprint arXiv:2402.09906, 2024

LUSIFER: Language universal space integra- tion for enhanced multilingual text embedding mod- els. InProceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval. Hieu Man and Huu Thien Nguyen. 2024. Counterfac- tual augmentation for robust authorship representa- tion learning. InProceedings of the 47th I...

work page arXiv 2024
[5]

InProceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 416–431, Dublin, Ireland

So different yet so alike! constrained unsuper- vised text style transfer. InProceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 416–431, Dublin, Ireland. Association for Computational Lin- guistics. Rafael A. Rivera-Soto, Olivia Elizabeth Miano, Juanita Ordonez, Barry Y . Chen, Aleem Khan...

work page arXiv 2021
[6]

Preprint, arXiv:2305.14902

M4: Multi-generator, multi-domain, and multi- lingual black-box machine-generated text detection. Preprint, arXiv:2305.14902. Anna Wegmann, Marijn Schraagen, and Dong Nguyen

work page arXiv
[7]

InPro- ceedings of the 7th Workshop on Representation Learning for NLP, pages 249–268, Dublin, Ireland

Same author or just same topic? towards content-independent style representations. InPro- ceedings of the 7th Workshop on Representation Learning for NLP, pages 249–268, Dublin, Ireland. Association for Computational Linguistics. Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Bal...
[8]

Qwen2 Technical Report

Taxonomy of risks posed by language models. InProceedings of the 2022 ACM Conference on Fair- ness, Accountability, and Transparency, FAccT ’22, page 214–229, New York, NY , USA. Association for Computing Machinery. John Wieting, Jonathan Clark, William Cohen, Graham Neubig, and Taylor Berg-Kirkpatrick. 2023. Beyond contrastive learning: A variational gen...

work page internal anchor Pith review arXiv 2022
[9]

’determination’: ’same author’ or ’differ- ent author’
[10]

’explaination’: Your analysis explaining why the texts appear to be written by the same author or different authors. The style representations for Text 1, Text 2, respectively, are: Text 1’s style representation: <placeholder> Text 2’s style representation: <placeholder> Content Discrimination Prompt Template: Given two content representations, deter- min...
[11]

’explaination’: A concise explanation justifying your determination, highlighting key similarities or differences in content
[12]

’determination’: Either ’same content’ or ’different content’ The content representations for Text 1, Text 2, respectively, are: Text 1’s content representation: <place- holder> Text 2’s content representation: <place- holder>