IDIOLEX: Unified and Continuous Representations for Idiolectal and Stylistic Variation
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866910105826492416 |
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| author | Kantharuban, Anjali Srivastava, Aarohi Faisal, Fahim Ahia, Orevaoghene Anastasopoulos, Antonios Chiang, David Tsvetkov, Yulia Neubig, Graham |
| author_facet | Kantharuban, Anjali Srivastava, Aarohi Faisal, Fahim Ahia, Orevaoghene Anastasopoulos, Antonios Chiang, David Tsvetkov, Yulia Neubig, Graham |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_04704 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | IDIOLEX: Unified and Continuous Representations for Idiolectal and Stylistic Variation Kantharuban, Anjali Srivastava, Aarohi Faisal, Fahim Ahia, Orevaoghene Anastasopoulos, Antonios Chiang, David Tsvetkov, Yulia Neubig, Graham Computation and Language 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. |
| title | IDIOLEX: Unified and Continuous Representations for Idiolectal and Stylistic Variation |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2604.04704 |