KRONE derives semantic execution hierarchies from flat logs to enable modular multi-level anomaly detection with hybrid local and nested-aware detectors plus limited LLM use, delivering 10% F1 gains and over 100x data efficiency on benchmarks and industrial data.
PromptNER: Prompting for named entity recognition
10 Pith papers cite this work, alongside 24 external citations. Polarity classification is still indexing.
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24
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AutoSpecNER is a new fine-grained NER dataset for vehicle advertisements with 659 examples and 15 categories, where DeBERTa reaches 90% micro-F1 versus 43% for rules and 77.8% for the best LLM.
Many-shot ICL with LLMs matches or exceeds supervised BERT on NER and generates high-quality labels for low-resource settings, producing ~10% absolute F1 gains when used to fine-tune BERT.
IRC-Bench pairs 25,136 entity-grounded and entity-elided reminiscence narratives so models must recover Wikidata entities from dispersed contextual cues rather than local mentions.
YoNER supplies a multi-domain Yoruba NER corpus of 5k sentences plus OyoBERT, showing African-centric models beat multilingual baselines in-domain while cross-domain performance drops sharply for blogs and movies.
LLMs exhibit 20-40% lower recall on ambiguous human names for PII detection, worsening under prompt injections, as shown via the new AmBench benchmark.
DynamicNER is a dynamic-categorization multilingual NER dataset with 155 entity types paired with CascadeNER, a two-stage lightweight LLM method claiming higher fine-grained accuracy.
Decomposing annotation tasks using centers from centering theory reduces aggregate inferential load via a degrees-of-freedom model and enables better sub-task allocation.
Small language models extract structured information from paediatric renal biopsy reports at up to 84.3% accuracy on CPU hardware with minimal clinician review.
A multi-step LLM-based pipeline constructs the first knowledge graph for nuclear fusion energy and enables RAG for multi-hop queries.
citing papers explorer
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KRONE: Scalable LLM-Augmented Log Anomaly Detection via Hierarchical Abstraction
KRONE derives semantic execution hierarchies from flat logs to enable modular multi-level anomaly detection with hybrid local and nested-aware detectors plus limited LLM use, delivering 10% F1 gains and over 100x data efficiency on benchmarks and industrial data.
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AutoSpecNER: A Fine-Grained Named Entity Recognition Dataset for Vehicle Specification Extraction
AutoSpecNER is a new fine-grained NER dataset for vehicle advertisements with 659 examples and 15 categories, where DeBERTa reaches 90% micro-F1 versus 43% for rules and 77.8% for the best LLM.
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Scaling Performance and Low-Resource Annotation with Many-Shot In-Context Learning for Named Entity Recognition
Many-shot ICL with LLMs matches or exceeds supervised BERT on NER and generates high-quality labels for low-resource settings, producing ~10% absolute F1 gains when used to fine-tune BERT.
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IRC-Bench: Recognizing Entities from Contextual Cues in First-Person Reminiscences
IRC-Bench pairs 25,136 entity-grounded and entity-elided reminiscence narratives so models must recover Wikidata entities from dispersed contextual cues rather than local mentions.
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YoNER: A New Yor\`ub\'a Multi-domain Named Entity Recognition Dataset
YoNER supplies a multi-domain Yoruba NER corpus of 5k sentences plus OyoBERT, showing African-centric models beat multilingual baselines in-domain while cross-domain performance drops sharply for blogs and movies.
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Can Large Language Models Really Recognize Your Name?
LLMs exhibit 20-40% lower recall on ambiguous human names for PII detection, worsening under prompt injections, as shown via the new AmBench benchmark.
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DynamicNER: A Dynamic, Multilingual, and Fine-Grained Dataset for LLM-based Named Entity Recognition
DynamicNER is a dynamic-categorization multilingual NER dataset with 155 entity types paired with CascadeNER, a two-stage lightweight LLM method claiming higher fine-grained accuracy.
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Task Decomposition for Efficient Annotation
Decomposing annotation tasks using centers from centering theory reduces aggregate inferential load via a degrees-of-freedom model and enables better sub-task allocation.
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A Semi-Automated Annotation Workflow for Paediatric Histopathology Reports Using Small Language Models
Small language models extract structured information from paediatric renal biopsy reports at up to 84.3% accuracy on CPU hardware with minimal clinician review.
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Automated Construction of a Knowledge Graph of Nuclear Fusion Energy for Effective Elicitation and Retrieval of Information
A multi-step LLM-based pipeline constructs the first knowledge graph for nuclear fusion energy and enables RAG for multi-hop queries.