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Llama-3.2-3B model achieves 92% accuracy in parsing blood donation requests

By PulseAugur Editorial · [1 sources] ·

Researchers have developed the Cognitive Blood Request System (CBRS), a framework designed to efficiently filter and parse urgent blood donation requests from social media streams. This system utilizes a novel bilingual dataset of over 11,000 messages in Bengali and English, incorporating adversarial negatives to enhance robustness. CBRS achieves 99% accuracy in filtering and a 92% zero-shot accuracy in parsing using a LoRA finetuned Llama-3.2-3B model, significantly outperforming other large language models while reducing token usage. AI

IMPACT Improves efficiency and inclusivity for time-sensitive information extraction tasks on social media.

RANK_REASON This is a research paper detailing a new system and dataset for information extraction. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Llama-3.2-3B model achieves 92% accuracy in parsing blood donation requests

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This is a research paper detailing a new system and dataset for information extraction. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Anik Saha, Mst. Fahmida Sultana Naznin, Zia Ul Hassan Abdullah, Anisa Binte Asad, K. G. Subarno Bithi, A. B. M. Alim Al Islam ·

    CBRS: Cognitive Blood Request System with Bilingual Dataset and Dual-Layer Filtering for Multi-Platform Social Streams

    arXiv:2604.16665v2 Announce Type: replace Abstract: Urgent blood donation seeking posts and messages on social media often go unnoticed due to the overwhelming volume of daily communications. Traditional app-based systems, reliant on manual input, struggle to reach users in low-r…

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