A blocking-plus-LLM-matching method delivers higher precision and broader coverage than threshold or top-K baselines while maintaining comparable recall on ICD version mapping tasks.
A survey on large language models with multilingualism: Recent advances and new frontiers
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
Hugging Face discussions show that access barriers, output quality, and setup complexity are the main user concerns for both general and multimodal LLMs.
Sentra-Guard reports 99.96% detection of adversarial LLM prompts with AUC 1.00 and ASR of 0.004% using a hybrid SBERT-FAISS and transformer classifier architecture with multilingual translation and human feedback.
citing papers explorer
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Managing Map Cardinality in Automatic Disease Classification Mapping: Balancing Precision, Recall and Coverage
A blocking-plus-LLM-matching method delivers higher precision and broader coverage than threshold or top-K baselines while maintaining comparable recall on ICD version mapping tasks.
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An Empirical Study of Perceptions of General LLMs and Multimodal LLMs on Hugging Face
Hugging Face discussions show that access barriers, output quality, and setup complexity are the main user concerns for both general and multimodal LLMs.
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Sentra-Guard: A Real-Time Multilingual Defense Against Adversarial LLM Prompts
Sentra-Guard reports 99.96% detection of adversarial LLM prompts with AUC 1.00 and ASR of 0.004% using a hybrid SBERT-FAISS and transformer classifier architecture with multilingual translation and human feedback.