IDIOLEX: Unified and Continuous Representations for Idiolectal and Stylistic Variation

Aarohi Srivastava; Anjali Kantharuban; Antonios Anastasopoulos; David Chiang; Fahim Faisal; Graham Neubig; Orevaoghene Ahia; Yulia Tsvetkov

arxiv: 2604.04704 · v1 · submitted 2026-04-06 · 💻 cs.CL

IDIOLEX: Unified and Continuous Representations for Idiolectal and Stylistic Variation

Anjali Kantharuban , Aarohi Srivastava , Fahim Faisal , Orevaoghene Ahia , Antonios Anastasopoulos , David Chiang , Yulia Tsvetkov , Graham Neubig This is my paper

Pith reviewed 2026-05-10 19:24 UTC · model grok-4.3

classification 💻 cs.CL

keywords idiolectal representation learningstyle and dialectsentence embeddingsprovenance supervisionArabic dialectsSpanish dialectslanguage model alignmentstylistic variation

0 comments

The pith

IDIOLEX learns continuous representations of sentence style and dialect by combining provenance supervision with linguistic features of content.

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

The paper proposes a training framework that produces sentence embeddings focused on how language is used rather than the ideas expressed. It draws on signals about a sentence's source, such as author or community origin, together with explicit linguistic markers to isolate stylistic and dialectal patterns. Experiments on Arabic and Spanish dialects show these vectors reflect real variation and can be reused in new settings for tasks like classification. The same vectors can also guide language models toward particular styles during training. This joint view of personal and group-level language habits is presented as a practical route to more style-aware language technology.

Core claim

IDIOLEX is a framework that trains models to output unified continuous vectors for each sentence's idiolect and style by jointly using provenance labels and content-derived linguistic features, thereby decoupling those signals from semantic content and enabling transfer across domains for analysis, classification, and stylistically guided language-model training.

What carries the argument

IDIOLEX framework, which fuses sentence provenance supervision with linguistic feature signals to produce style-dialect embeddings decoupled from meaning.

If this is right

The representations transfer to new domains for dialect analysis and classification.
They can be used as auxiliary objectives to align language models with target styles.
Joint modeling of individual idiolect and community-level variation yields a useful perspective on stylistic differences.
The approach supports applications that require sensitivity to how language is expressed, such as building more diverse language models.

Where Pith is reading between the lines

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

The same decoupling technique could be tested on other language pairs or on non-text modalities to see whether the separation of style from content generalizes.
If the vectors prove stable, they might serve as a diagnostic tool for measuring how much stylistic drift occurs when models are fine-tuned on narrow data.
Downstream systems that currently ignore style might gain measurable gains in user satisfaction or accessibility by conditioning on these representations.

Load-bearing premise

Provenance information and linguistic features can be combined to separate stylistic and dialectal signals from semantic content in a way that yields transferable representations.

What would settle it

The learned vectors show no improvement over ordinary sentence embeddings on held-out style or dialect classification tasks, or they fail to improve downstream performance when used as training objectives for style alignment.

Figures

Figures reproduced from arXiv: 2604.04704 by Aarohi Srivastava, Anjali Kantharuban, Antonios Anastasopoulos, David Chiang, Fahim Faisal, Graham Neubig, Orevaoghene Ahia, Yulia Tsvetkov.

**Figure 1.** Figure 1: IDIOLEX used to compare the idiolectal alignment between user input and GPT 5.1 generations in casual Argentinian Spanish. Much of mainstream evaluation and optimization of large language models (LLMs) prioritizes semantic correctness (Clark et al., 2020; Lewis et al., 2020; Liu et al., 2023; Chiang et al., 2024; Singh et al., 2025; Kim et al., 2025). Userspecific linguistic and stylistic adaptation re… view at source ↗

**Figure 2.** Figure 2: IDIOLEX training framework. During training, all batches are sampled such that every individual item can act as an anchor for contrastive learning, necessitating that there are 23−n samples for each proximity score n ∈ [0, 3]. above, §2) with LLM-extracted linguistic features. Our goal is to encourage representations that are consistent with the view of stylistic variation described in Section 2. Rather t… view at source ↗

**Figure 3.** Figure 3: Non-fine-tuned IDIOLEX classification model’s likelihood distribution over samples on multilabel Spanish DID. la Rosa et al., 2022). Data from 10 authors from each dialect is withheld for each of the development and test sets (150 in total per set). Hyperparameters, model configurations, and optimization settings are reported in Appendix B.1 to facilitate reproducibility. 4 Performance on Classification … view at source ↗

**Figure 5.** Figure 5: Stylistic similarity on the MADAR26 dataset calculated via the Arabic IDIOLEX model on sentences with identical semantic content. We see clear differentiation by dialectal proximity. In [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗

read the original abstract

Existing sentence representations primarily encode what a sentence says, rather than how it is expressed, even though the latter is important for many applications. In contrast, we develop sentence representations that capture style and dialect, decoupled from semantic content. We call this the task of idiolectal representation learning. We introduce IDIOLEX, a framework for training models that combines supervision from a sentence's provenance with linguistic features of a sentence's content, to learn a continuous representation of each sentence's style and dialect. We evaluate the approach on dialects of both Arabic and Spanish. The learned representations capture meaningful variation and transfer across domains for analysis and classification. We further explore the use of these representations as training objectives for stylistically aligning language models. Our results suggest that jointly modeling individual and community-level variation provides a useful perspective for studying idiolect and supports downstream applications requiring sensitivity to stylistic differences, such as developing diverse and accessible LLMs.

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

IDIOLEX frames a new training mix of provenance signals and linguistic features for continuous idiolectal representations, but the decoupling from semantics looks vulnerable to the usual content confounds in dialect data.

read the letter

The core contribution is a framework that supervises sentence embeddings with both sentence provenance and content-derived linguistic features to produce a continuous space meant to encode style and dialect separately from meaning. They test this on Arabic and Spanish dialect data, show some transfer across domains, and try using the representations as an alignment objective for language models. The joint modeling of individual and community-level signals is a sensible way to approach idiolect, and running the same setup on two languages gives the results a bit more weight than single-language work usually carries. The downstream angle toward stylistically diverse LLMs is also practical. The main weakness is that the decoupling step rests on an assumption that provenance plus linguistic features will strip out semantic content. Dialect corpora routinely tie regional markers to topics or lexical choices that carry meaning, and nothing in the abstract or the described setup indicates adversarial removal, topic balancing, or ablations that would confirm the signals are isolated. If the full paper does not include those checks, the representations may still be picking up content proxies rather than pure idiolect. This is the sort of paper that belongs in a reading group focused on style, dialect, or representation learning for inclusive models. Readers working on those topics would find the framing and the transfer experiments useful even if they end up wanting tighter controls. It is worth sending to peer review because the combination of signals is new enough and the application area is relevant enough that referees can usefully pressure the evidence on decoupling.

Referee Report

2 major / 2 minor

Summary. The paper introduces IDIOLEX, a framework for learning continuous sentence representations that capture idiolectal and stylistic variation decoupled from semantic content. It combines supervision from a sentence's provenance with linguistic features of its content, evaluates the approach on dialects of Arabic and Spanish, demonstrates that the representations capture meaningful variation and transfer across domains for analysis and classification tasks, and explores their use as training objectives for stylistically aligning language models. The results suggest that jointly modeling individual and community-level variation is useful for studying idiolect and for applications requiring stylistic sensitivity.

Significance. If the decoupling of stylistic/idiolectal signals from semantics holds and the representations transfer effectively, the work could offer a valuable new perspective on idiolectal modeling in NLP and support practical applications such as building more diverse and accessible LLMs. The emphasis on continuous, unified representations for both individual and community variation addresses an important gap in existing sentence embedding methods that focus primarily on semantics.

major comments (2)

[Abstract] The central claim that provenance supervision combined with linguistic features reliably decouples stylistic/idiolectal signals from semantic content (Abstract) lacks supporting mechanisms such as adversarial objectives or topic-balanced sampling. Dialect corpora frequently confound style markers with topic or lexical semantics, so without explicit controls the learned space may encode content proxies rather than pure variation.
[Abstract] No equations, training objectives, ablation studies, or quantitative metrics (e.g., classification accuracies, transfer results, or alignment scores) are provided to verify whether the representations actually isolate idiolectal variation or support the cross-domain transfer and LM alignment claims.

minor comments (2)

[Abstract] The abstract mentions evaluation on Arabic and Spanish dialects but does not specify the datasets, splits, or baseline comparisons used.
[Abstract] Notation for the learned representations (e.g., how provenance and linguistic features are combined into the continuous vector) is not defined.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed feedback on our work. We address each major comment below, providing clarifications based on the manuscript while acknowledging areas where additional discussion can strengthen the presentation.

read point-by-point responses

Referee: [Abstract] The central claim that provenance supervision combined with linguistic features reliably decouples stylistic/idiolectal signals from semantic content (Abstract) lacks supporting mechanisms such as adversarial objectives or topic-balanced sampling. Dialect corpora frequently confound style markers with topic or lexical semantics, so without explicit controls the learned space may encode content proxies rather than pure variation.

Authors: We appreciate this concern regarding potential confounding in dialect data. Our approach uses provenance supervision to directly target idiolectal and stylistic signals at both individual and community levels, paired with linguistic features (e.g., syntactic and lexical markers independent of content) to steer the representation away from semantics. While we do not incorporate adversarial objectives or topic-balanced sampling, the cross-domain transfer results and downstream task performance on Arabic and Spanish dialects indicate that the learned space prioritizes variation over content proxies. We will revise the manuscript to include an explicit discussion of this design choice, its limitations relative to adversarial methods, and supporting evidence from the evaluations. revision: partial
Referee: [Abstract] No equations, training objectives, ablation studies, or quantitative metrics (e.g., classification accuracies, transfer results, or alignment scores) are provided to verify whether the representations actually isolate idiolectal variation or support the cross-domain transfer and LM alignment claims.

Authors: The abstract is intentionally high-level and omits equations, objectives, and metrics per standard conventions. The full manuscript details the training objectives (combining provenance loss with linguistic feature alignment), model architecture, ablation studies on the contribution of each component, and quantitative results including classification accuracies, cross-domain transfer performance, and LM alignment scores on the Arabic and Spanish datasets. These directly support the claims of decoupling and utility for transfer and alignment. We will update the abstract to reference key quantitative findings for improved clarity. revision: partial

Circularity Check

0 steps flagged

No circularity: framework is an empirical training proposal without reductive derivations

full rationale

The paper presents IDIOLEX as a novel training framework that combines sentence provenance supervision with linguistic features to produce continuous idiolectal/stylistic representations decoupled from semantics. No equations, first-principles derivations, fitted parameters renamed as predictions, or load-bearing self-citations appear in the abstract or description. The central claims rest on the design of the framework itself and its empirical evaluation on Arabic and Spanish dialect data, with no reduction of outputs to inputs by construction. This is a standard machine-learning method introduction whose validity is assessed via downstream tasks rather than tautological redefinition.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only view yields no identifiable free parameters, axioms, or invented entities; full methods section would be required to audit these.

pith-pipeline@v0.9.0 · 5490 in / 1175 out tokens · 41103 ms · 2026-05-10T19:24:10.747380+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

We introduce IDIOLEX, a framework for training models that combines supervision from a sentence's provenance with linguistic features of a sentence's content, to learn a continuous representation of each sentence's style and dialect.

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

Where Does Authorship Signal Emerge in Encoder-Based Language Models?
cs.CL 2026-05 unverdicted novelty 7.0

Scoring mechanism determines the layer at which encoder-based models consolidate authorship signals, with mean pooling acting early and late interaction deferring to later layers.

Reference graph

Works this paper leans on

8 extracted references · 8 canonical work pages · cited by 1 Pith paper · 1 internal anchor

[1]

doi: 10.18653/v1/W19-4632

Association for Computational Linguistics. doi: 10.18653/v1/W19-4632. URL https://aclanthology.org/W19-4632/. Tareq Al-Moslmi, Mohammed Albared, Adel Al-Shabi, Nazlia Omar, and Salwani Ab- dullah. Arabic senti-lexicon: Constructing publicly available language resources for arabic sentiment analysis.Journal of Information Science, 44(3):345–362, 2018. doi:...

work page doi:10.18653/v1/w19-4632 2018
[2]

The Llama 3 Herd of Models

Association for Computational Linguistics. URL https://aclanthology.org/2026. vardial-1.30/. Yi Dong, Zhilin Wang, Makesh Sreedhar, Xianchao Wu, and Oleksii Kuchaiev. SteerLM: Attribute conditioned SFT as an (user-steerable) alternative to RLHF. In Houda Bouamor, Juan Pino, and Kalika Bali (eds.),Findings of the Association for Computational Linguistics: ...

work page internal anchor Pith review Pith/arXiv arXiv doi:10.18653/v1/2023.findings-emnlp.754 2026
[3]

Same Author or Just Same Topic? Towards Content-Independent Style Representations , shorttitle =

Association for Computational Linguistics. doi: 10.18653/v1/2022.repl4nlp-1.26. URLhttps://aclanthology.org/2022.repl4nlp-1.26/. 16 Preprint. Under review. Charles Welch, Jonathan K. Kummerfeld, Ver´onica P´erez-Rosas, and Rada Mihalcea. Com- positional demographic word embeddings. In Bonnie Webber, Trevor Cohn, Yulan He, and Yang Liu (eds.),Proceedings o...

work page doi:10.18653/v1/2022.repl4nlp-1.26 2022
[4]

Same Subreddit: A sentence from the same subreddit/region but written by a differ- ent author.r=1

work page
[5]

r=0 7gpt-5-mini-2025-08-07 19 Preprint

Different Subreddit: A sentence from a different subreddit/region versus the anchor. r=0 7gpt-5-mini-2025-08-07 19 Preprint. Under review. Arabic Binary Features Morphosyntax & Clause Structure contains case endings u a i contains future prefix sa contains tanwin un an in contains future particle sawfa contains dual suffix an or ayn contains dialectal fut...

work page 2025
[6]

Under review

for Spanish and AraBERTv2 (Antoun et al., 2020) for Arabic (the same monolingual BERT-based models used in our closed-set baseline), as well as Multilingual E5 (Wang et al., 2024), a state-of-the-art multilingual sentence-embedding model commonly used in recent 24 Preprint. Under review. embedding evaluation work. Using frozen encoder representations, we ...

work page 2020
[7]

English → DA: ”Translate this to [dialect]: [source]”, ”Say this in [dialect]: [source]”, ”How would you say this in [dialect]? [source]”

work page
[8]

This yields high-quality, naturally dialectal training pairs

MSA → DA: ”Convert this to [dialect]: [source]”, ”Rewrite this in [dialect]: [source]” The dialectal translation serves as the ground-truth response. This yields high-quality, naturally dialectal training pairs. 25 Preprint. Under review. Source Type Dialects Size Method MADAR-26 (train) (Bouamor et al., 2018) Bitext Multi-Dialectal 2k sentences per Templ...

work page 2018

[1] [1]

doi: 10.18653/v1/W19-4632

Association for Computational Linguistics. doi: 10.18653/v1/W19-4632. URL https://aclanthology.org/W19-4632/. Tareq Al-Moslmi, Mohammed Albared, Adel Al-Shabi, Nazlia Omar, and Salwani Ab- dullah. Arabic senti-lexicon: Constructing publicly available language resources for arabic sentiment analysis.Journal of Information Science, 44(3):345–362, 2018. doi:...

work page doi:10.18653/v1/w19-4632 2018

[2] [2]

The Llama 3 Herd of Models

Association for Computational Linguistics. URL https://aclanthology.org/2026. vardial-1.30/. Yi Dong, Zhilin Wang, Makesh Sreedhar, Xianchao Wu, and Oleksii Kuchaiev. SteerLM: Attribute conditioned SFT as an (user-steerable) alternative to RLHF. In Houda Bouamor, Juan Pino, and Kalika Bali (eds.),Findings of the Association for Computational Linguistics: ...

work page internal anchor Pith review Pith/arXiv arXiv doi:10.18653/v1/2023.findings-emnlp.754 2026

[3] [3]

Same Author or Just Same Topic? Towards Content-Independent Style Representations , shorttitle =

Association for Computational Linguistics. doi: 10.18653/v1/2022.repl4nlp-1.26. URLhttps://aclanthology.org/2022.repl4nlp-1.26/. 16 Preprint. Under review. Charles Welch, Jonathan K. Kummerfeld, Ver´onica P´erez-Rosas, and Rada Mihalcea. Com- positional demographic word embeddings. In Bonnie Webber, Trevor Cohn, Yulan He, and Yang Liu (eds.),Proceedings o...

work page doi:10.18653/v1/2022.repl4nlp-1.26 2022

[4] [4]

Same Subreddit: A sentence from the same subreddit/region but written by a differ- ent author.r=1

work page

[5] [5]

r=0 7gpt-5-mini-2025-08-07 19 Preprint

Different Subreddit: A sentence from a different subreddit/region versus the anchor. r=0 7gpt-5-mini-2025-08-07 19 Preprint. Under review. Arabic Binary Features Morphosyntax & Clause Structure contains case endings u a i contains future prefix sa contains tanwin un an in contains future particle sawfa contains dual suffix an or ayn contains dialectal fut...

work page 2025

[6] [6]

Under review

for Spanish and AraBERTv2 (Antoun et al., 2020) for Arabic (the same monolingual BERT-based models used in our closed-set baseline), as well as Multilingual E5 (Wang et al., 2024), a state-of-the-art multilingual sentence-embedding model commonly used in recent 24 Preprint. Under review. embedding evaluation work. Using frozen encoder representations, we ...

work page 2020

[7] [7]

English → DA: ”Translate this to [dialect]: [source]”, ”Say this in [dialect]: [source]”, ”How would you say this in [dialect]? [source]”

work page

[8] [8]

This yields high-quality, naturally dialectal training pairs

MSA → DA: ”Convert this to [dialect]: [source]”, ”Rewrite this in [dialect]: [source]” The dialectal translation serves as the ground-truth response. This yields high-quality, naturally dialectal training pairs. 25 Preprint. Under review. Source Type Dialects Size Method MADAR-26 (train) (Bouamor et al., 2018) Bitext Multi-Dialectal 2k sentences per Templ...

work page 2018