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        <title>Textual Dataset Distillation via Language Model Embedding</title>
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        <namePart type="given">Yefan</namePart>
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        <namePart type="given">Luyang</namePart>
        <namePart type="family">Kong</namePart>
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    <name type="personal">
        <namePart type="given">Andrey</namePart>
        <namePart type="family">Kan</namePart>
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    <name type="personal">
        <namePart type="given">Laurent</namePart>
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            <namePart type="family">Al-Onaizan</namePart>
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            <namePart type="given">Mohit</namePart>
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            <namePart type="given">Yun-Nung</namePart>
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    <abstract>Dataset distillation is a process aimed at condensing datasets while preserving essential characteristics. In the text domain, prevailing methods typically generate distilled data as embedding vectors, which are not human-readable. This approach simplifies optimization but limits the transferability of distilled data across different model architectures. To address this limitation, we introduce a model-agnostic, data-efficient method that leverages Language Model (LM) embeddings. Compared to parameter-efficient methods such as LORA, our approach achieves comparable performance with significantly faster processing times. We evaluate our methodology through classification tasks on datasets like IMDB and AG-News, demonstrating performance that is on par with or exceeds previous model-dependent techniques. By utilizing LM embeddings, our method offers enhanced flexibility and improved transferability, expanding the range of potential applications.</abstract>
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        <date>2024-11</date>
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%0 Conference Proceedings
%T Textual Dataset Distillation via Language Model Embedding
%A Tao, Yefan
%A Kong, Luyang
%A Kan, Andrey
%A Callot, Laurent
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F tao-etal-2024-textual
%X Dataset distillation is a process aimed at condensing datasets while preserving essential characteristics. In the text domain, prevailing methods typically generate distilled data as embedding vectors, which are not human-readable. This approach simplifies optimization but limits the transferability of distilled data across different model architectures. To address this limitation, we introduce a model-agnostic, data-efficient method that leverages Language Model (LM) embeddings. Compared to parameter-efficient methods such as LORA, our approach achieves comparable performance with significantly faster processing times. We evaluate our methodology through classification tasks on datasets like IMDB and AG-News, demonstrating performance that is on par with or exceeds previous model-dependent techniques. By utilizing LM embeddings, our method offers enhanced flexibility and improved transferability, expanding the range of potential applications.
%U https://aclanthology.org/2024.findings-emnlp.733
%P 12557-12569

Markdown (Informal)

[Textual Dataset Distillation via Language Model Embedding](https://aclanthology.org/2024.findings-emnlp.733) (Tao et al., Findings 2024)

• Textual Dataset Distillation via Language Model Embedding (Tao et al., Findings 2024)

ACL

• Yefan Tao, Luyang Kong, Andrey Kan, and Laurent Callot. 2024. Textual Dataset Distillation via Language Model Embedding. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 12557–12569, Miami, Florida, USA. Association for Computational Linguistics.