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        <title>Mixture-of-Linguistic-Experts Adapters for Improving and Interpreting Pre-trained Language Models</title>
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        <namePart type="given">Raymond</namePart>
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    <name type="personal">
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        <namePart type="family">Carenini</namePart>
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            <namePart type="given">Kalika</namePart>
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    <abstract>In this work, we propose a method that combines two popular research areas by injecting linguistic structures into pre-trained language models in the parameter-efficient fine-tuning (PEFT) setting. In our approach, parallel adapter modules encoding different linguistic structures are combined using a novel Mixture-of-Linguistic-Experts architecture, where Gumbel-Softmax gates are used to determine the importance of these modules at each layer of the model. To reduce the number of parameters, we first train the model for a fixed small number of steps before pruning the experts based on their important scores. Our experiment results with three different pre-trained models show that our approach can outperform state-of-the-art PEFT methods with a comparable number of parameters. In addition, we provide additional analysis to examine the experts selected by each model at each layer to provide insights for future studies.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-emnlp.634</identifier>
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%0 Conference Proceedings
%T Mixture-of-Linguistic-Experts Adapters for Improving and Interpreting Pre-trained Language Models
%A Li, Raymond
%A Murray, Gabriel
%A Carenini, Giuseppe
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F li-etal-2023-mixture
%X In this work, we propose a method that combines two popular research areas by injecting linguistic structures into pre-trained language models in the parameter-efficient fine-tuning (PEFT) setting. In our approach, parallel adapter modules encoding different linguistic structures are combined using a novel Mixture-of-Linguistic-Experts architecture, where Gumbel-Softmax gates are used to determine the importance of these modules at each layer of the model. To reduce the number of parameters, we first train the model for a fixed small number of steps before pruning the experts based on their important scores. Our experiment results with three different pre-trained models show that our approach can outperform state-of-the-art PEFT methods with a comparable number of parameters. In addition, we provide additional analysis to examine the experts selected by each model at each layer to provide insights for future studies.
%R 10.18653/v1/2023.findings-emnlp.634
%U https://aclanthology.org/2023.findings-emnlp.634/
%U https://doi.org/10.18653/v1/2023.findings-emnlp.634
%P 9456-9469

Markdown (Informal)

[Mixture-of-Linguistic-Experts Adapters for Improving and Interpreting Pre-trained Language Models](https://aclanthology.org/2023.findings-emnlp.634/) (Li et al., Findings 2023)

• Mixture-of-Linguistic-Experts Adapters for Improving and Interpreting Pre-trained Language Models (Li et al., Findings 2023)

ACL

• Raymond Li, Gabriel Murray, and Giuseppe Carenini. 2023. Mixture-of-Linguistic-Experts Adapters for Improving and Interpreting Pre-trained Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 9456–9469, Singapore. Association for Computational Linguistics.