Embedded Named Entity Recognition using Probing Classifiers

• URL: http://arxiv.org/abs/2403.11747v2
• Date: Mon, 14 Oct 2024 09:56:19 GMT
• Title: Embedded Named Entity Recognition using Probing Classifiers
• Authors: Nicholas Popovič, Michael Färber,
• Abstract summary: EMBER enables streaming named entity recognition in decoder-only language models without fine-tuning them. We show that EMBER maintains high token generation rates, with only a negligible decrease in speed of around 1%. We make our code and data available online, including a toolkit for training, testing, and deploying efficient token classification models.
• Score: 10.573861741540853
• License:
• 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.

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