LP Data Pipeline: Lightweight, Purpose-driven Data Pipeline for Large Language Models (2025.emnlp-industry)
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| Challenge: | Creating high-quality datasets for large language models often relies on resource-intensive, GPU-accelerated models for quality filtering, making the process time-consuming and costly. |
| Approach: | They propose a framework that operates entirely on CPUs to streamline the processes of dataset extraction, filtering, and curation. |
| Outcome: | The proposed framework reduces preparation time and costs while maintaining high data quality while enhancing the applicability of LLMs in specialized contexts. |
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