Papers with domain adaptation framework
Domain Adaptation with BERT-based Domain Classification and Data Selection (D19-61)
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| Challenge: | Modern deep neural models with millions of parameters can easily adapt to a new learning task and dataset when enough supervision is given. |
| Approach: | They propose a domain adaptation framework based on curriculum learning and domain-discriminative data selection. |
| Outcome: | The proposed framework outperforms discrepancy-based methods on transfer tasks while consuming only fraction of training budget. |
Reformulating Domain Adaptation of Large Language Models as Adapt-Retrieve-Revise: A Case Study on Chinese Legal Domain (2024.findings-acl)
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| Challenge: | Recent large language models like GPT-4 have demonstrated astonishing zero-shot capabilities in general domain tasks, but they often generate content with hallucinations in specific domains such as Chinese law. |
| Approach: | They propose a framework for adapting large language models (LLMs) to Chinese legal domains by reformulating generation as an adapt-retrieve-revise process. |
| Outcome: | The proposed framework outperforms existing models in the Chinese legal domain by +33.6 points in the zero-shot setting. |
Examining and Adapting Time for Multilingual Classification via Mixture of Temporal Experts (2025.naacl-long)
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| Challenge: | Existing classification models only consider the temporal variations of existing data . current models focus on English corpora, leaving time as domains unexplored . |
| Approach: | They propose a framework to generalize classifiers over time on four languages, English, Danish, French, and German. |
| Outcome: | The proposed framework can generalize classifiers over time on four languages, English, Danish, French, and German. |
Domain Adaptation for Conversational Query Production with the RAG Model Feedback (2023.findings-emnlp)
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| Challenge: | Existing studies have focused on human-annotated search queries but they can not cover conversations of various domains. |
| Approach: | They propose a domain adaptation framework that uses retrieval-augmented generation to improve the model's robustness. |
| Outcome: | The proposed model is more robust and performs significantly better in a more challenging setting over strong baselines. |