mlsanityData to 14 Oct 2026

DADA: Dialect Adaptation via Dynamic Aggregation of Linguistic Rules

arXiv 2305.13406 ↗First posted 22 May 2023cs.CLcited by 13

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Abstract (arXiv)

Existing large language models (LLMs) that mainly focus on Standard American English (SAE) often lead to significantly worse performance when being applied to other English dialects. While existing mitigations tackle discrepancies for individual target dialects, they assume access to high-accuracy dialect identification systems. The boundaries between dialects are inherently flexible, making it difficult to categorize language into discrete predefined categories. In this paper, we propose DADA (Dialect Adaptation via Dynamic Aggregation), a modular approach to imbue SAE-trained models with multi-dialectal robustness by composing adapters which handle specific linguistic features. The compositional architecture of DADA allows for both targeted adaptation to specific dialect variants and simultaneous adaptation to various dialects. We show that DADA is effective for both single task and instruction finetuned language models, offering an extensible and interpretable framework for adapting existing LLMs to different English dialects.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 42 of its 67 references that are in the map. First posted on arXiv 22 May 2023.

Builds on

42 of 67 references.

References · 42

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 5; 2025: 3; 2026: 2.

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  1. 2024Natural Language Processing for Dialects of a Language: A Surveycited by 40
  2. 2024Culturally Aware and Adapted NLP: A Taxonomy and a Survey of the State of the Artcited by 26
  3. 2023Confronting LLMs with Traditional ML: Rethinking the Fairness of Large Language Models in Tabular Classificationscited by 15
  4. 2024Evaluating Dialect Robustness of Language Models via Conversation Understandingcited by 14
  5. 2023Task-Agnostic Low-Rank Adapters for Unseen English Dialectscited by 9
  6. 2024Predicting the Target Word of Game-playing Conversations using a Low-Rank Dialect Adapter for Decoder Modelscited by 6
  7. 2025Analyzing Dialectical Biases in LLMs for Knowledge and Reasoning Benchmarkscited by 5
  8. 2023MC2: Towards Transparent and Culturally-Aware NLP for Minority Languages in Chinacited by 4
  9. 2024Performance in a dialectal profiling task of LLMs for varieties of Brazilian Portuguesecited by 3
  10. 2025A Multi-Agent Framework for Mitigating Dialect Biases in Privacy Policy Question-Answering Systemscited by 2
  11. 2025Harnessing Test-time Adaptation for NLU tasks Involving Dialects of Englishcited by 1
  12. 2026DialectLLM: A Dialect-Aware Dialog[ue] Generation Framework Beyond Standard American Englishcited by 0
1 more
  1. 2026Metric-Construction Coupling Inflates Measured Synthetic Dialect Recoverycited by 0

Similar, not cited

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  1. 2026DiaLLM: An Investigation into the Robustness-Generation Gap in English Dialect Adaptation
  2. 2024Data-Augmentation-Based Dialectal Adaptation for LLMs
  3. 2026The Dialect Tax: Dialectal Biases Persist throughout the Language Modeling Pipeline
  4. 2025DiaMoE-TTS: A Unified IPA-Based Dialect TTS Framework with Mixture-of-Experts and Parameter-Efficient Zero-Shot Adaptation
  5. 2025DialectGen: Benchmarking and Improving Dialect Robustness in Multimodal Generation
  6. 2025DialectalArabicMMLU: Benchmarking Dialectal Capabilities in Arabic and Multilingual Language Models
  7. 2026Efficiently Adapting Spoken Language Models for the Singaporean Context
  8. 2026On-Policy Self-Distillation for Multi-Dialect ASR: Mastering Dialects, Retaining Mandarin