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.
3
Pith papers citing it
representative citing papers
Across 662 annotated Hugging Face threads, gated access (dominated by Llama), multimodal generation quality, and deployment/invocation complexity are the most prominent user concerns.
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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When Models Meet Users: An Empirical Study of Perceptions of General LLMs and Multimodal LLMs on Hugging Face
Across 662 annotated Hugging Face threads, gated access (dominated by Llama), multimodal generation quality, and deployment/invocation complexity are the most prominent user concerns.
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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.