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        <title>Embedded Named Entity Recognition using Probing Classifiers</title>
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            <namePart type="given">Yaser</namePart>
            <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>Streaming text generation, has become a common way of increasing the responsiveness of language model powered applications such as chat assistants. At the same time, extracting semantic information from generated text is a useful tool for applications such as automated fact checking or retrieval augmented generation. Currently, this requires either separate models during inference, which increases computational cost, or destructive fine-tuning of the language model. Instead, we propose an approach called EMBER which enables streaming named entity recognition in decoder-only language models without fine-tuning them and while incurring minimal additional computational cost at inference time. Specifically, our experiments show that EMBER maintains high token generation rates, with only a negligible decrease in speed of around 1% compared to a 43.64% slowdown measured for a baseline. We make our code and data available online, including a toolkit for training, testing, and deploying efficient token classification models optimized for streaming text generation.</abstract>
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%0 Conference Proceedings
%T Embedded Named Entity Recognition using Probing Classifiers
%A Popovic, Nicholas
%A Färber, Michael
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F popovic-farber-2024-embedded
%X Streaming text generation, has become a common way of increasing the responsiveness of language model powered applications such as chat assistants. At the same time, extracting semantic information from generated text is a useful tool for applications such as automated fact checking or retrieval augmented generation. Currently, this requires either separate models during inference, which increases computational cost, or destructive fine-tuning of the language model. Instead, we propose an approach called EMBER which enables streaming named entity recognition in decoder-only language models without fine-tuning them and while incurring minimal additional computational cost at inference time. Specifically, our experiments show that EMBER maintains high token generation rates, with only a negligible decrease in speed of around 1% compared to a 43.64% slowdown measured for a baseline. We make our code and data available online, including a toolkit for training, testing, and deploying efficient token classification models optimized for streaming text generation.
%U https://aclanthology.org/2024.emnlp-main.988
%P 17830-17850

Markdown (Informal)

[Embedded Named Entity Recognition using Probing Classifiers](https://aclanthology.org/2024.emnlp-main.988) (Popovic & Färber, EMNLP 2024)

• Embedded Named Entity Recognition using Probing Classifiers (Popovic & Färber, EMNLP 2024)

ACL

• Nicholas Popovic and Michael Färber. 2024. Embedded Named Entity Recognition using Probing Classifiers. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 17830–17850, Miami, Florida, USA. Association for Computational Linguistics.