mlsanityData to 14 Oct 2026

TADA: Task-Agnostic Dialect Adapters for English

arXiv 2305.16651 ↗First posted 26 May 2023cs.CLcited by 15

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Abstract (arXiv)

Large Language Models, the dominant starting point for Natural Language Processing (NLP) applications, fail at a higher rate for speakers of English dialects other than Standard American English (SAE). Prior work addresses this using task-specific data or synthetic data augmentation, both of which require intervention for each dialect and task pair. This poses a scalability issue that prevents the broad adoption of robust dialectal English NLP. We introduce a simple yet effective method for task-agnostic dialect adaptation by aligning non-SAE dialects using adapters and composing them with task-specific adapters from SAE. Task-Agnostic Dialect Adapters (TADA) improve dialectal robustness on 4 dialectal variants of the GLUE benchmark without task-specific supervision.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 33 of its 56 references that are in the map. First posted on arXiv 26 May 2023.

Builds on

33 of 56 references.

References · 33

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21 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 6; 2025: 2; 2026: 5.

15 papers.

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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. 2024Evaluating Dialect Robustness of Language Models via Conversation Understandingcited by 14
  4. 2023Task-Agnostic Low-Rank Adapters for Unseen English Dialectscited by 9
  5. 2023BERTwich: Extending BERT's Capabilities to Model Dialectal and Noisy Textcited by 6
  6. 2024We're Calling an Intervention: Exploring Fundamental Hurdles in Adapting Language Models to Nonstandard Textcited by 6
  7. 2024Predicting the Target Word of Game-playing Conversations using a Low-Rank Dialect Adapter for Decoder Modelscited by 6
  8. 2024Exploring the Robustness of Task-oriented Dialogue Systems for Colloquial German Varietiescited by 4
  9. 2025A Multi-Agent Framework for Mitigating Dialect Biases in Privacy Policy Question-Answering Systemscited by 2
  10. 2025Harnessing Test-time Adaptation for NLU tasks Involving Dialects of Englishcited by 1
  11. 2026DiaLLM: An Investigation into the Robustness-Generation Gap in English Dialect Adaptationcited by 1
  12. 2026DialectLLM: A Dialect-Aware Dialog[ue] Generation Framework Beyond Standard American Englishcited by 0
3 more
  1. 2026AI systems and the reproduction of (standard) language ideologies in World Englishescited by 0
  2. 2026Different Perturbations, Different Mechanisms: Understanding Continued Pre-training for Zero-Shot Dialect Robustnesscited by 0
  3. 2026Not Safe for All: Auditing the Dialect Penalty in Text-to-Image Safety Pipelinescited by 0