AIBullisharXiv – CS AI · Jun 236/10
🧠Researchers demonstrate that large language models can match or exceed fine-tuned BERT performance on Named Entity Recognition tasks when provided with hundreds of in-context examples rather than just a few. The study shows many-shot in-context learning can also serve as a data annotation framework, generating high-quality training data that improves low-resource NER by ~10% F1 when used to fine-tune supervised models.
AINeutralarXiv – CS AI · Jun 235/10
🧠Researchers propose an explanation-guided framework for medical named entity recognition (NER) in Chinese atopic dermatitis clinical texts, using stability and boundary-aware constraints to improve model reliability and interpretability. The method combines perturbation-based analysis with adaptive fusion of local and global explanations, achieving performance gains across multiple NER models while enhancing explanation robustness for clinical decision support.
AIBullisharXiv – CS AI · Jun 106/10
🧠Researchers demonstrate that DeepSeek-R1-8B, enhanced with LoRA and NEFTune fine-tuning techniques, achieves 91.2% accuracy on financial named-entity recognition tasks, outperforming larger baseline models. This advance shows open-source models can match specialized financial AI capabilities through efficient adaptation methods.
🧠 Llama
AINeutralarXiv – CS AI · May 296/10
🧠Researchers developed a specialized Named Entity Recognition model for identifying disease-related clinical entities in immunology and infectious disease texts, achieving 0.89 F1 score through transformer-based architecture with clinical embeddings. The model outperforms general-purpose NLP systems and LLMs in extracting granular biomedical concepts from unstructured medical narratives, enabling improved cohort identification and clinical decision support.
AINeutralarXiv – CS AI · May 276/10
🧠Researchers have extended LELA, an LLM-based entity linking framework, into a practical Python library that combines zero-shot Named Entity Recognition with entity disambiguation. The end-to-end pipeline addresses limitations in existing approaches by offering domain-agnostic capabilities and demonstrating robust performance across diverse entity linking tasks, making it more applicable to real-world usage scenarios.
AIBullisharXiv – CS AI · Apr 206/10
🧠Researchers introduce DiZiNER, a framework that improves zero-shot named entity recognition by simulating human annotation disagreement processes using multiple LLMs. The approach achieves state-of-the-art results on 14 of 18 benchmarks, closing the performance gap between zero-shot and supervised systems by over 11 percentage points.
🧠 GPT-5
AIBullisharXiv – CS AI · Mar 26/1012
🧠Researchers developed TRIZ-RAGNER, a retrieval-augmented large language model framework that improves patent analysis and systematic innovation by extracting technical contradictions from patent documents. The system achieved 84.2% F1-score accuracy, outperforming existing methods by 7.3 percentage points through better integration of domain-specific knowledge.
AINeutralarXiv – CS AI · Mar 34/104
🧠Researchers have created CrimeNER, a specialized dataset of over 1,500 annotated crime-related documents for training named-entity recognition AI models. The study addresses the lack of quality training data in the crime domain by developing a database from terrorist attack reports and DOJ press notes, defining 22 types of crime-related entities.
