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This paper proposes LC-ICL, a novel few-shot technique that uses both correct and incorrect examples with error-cause labels to improve large language models' performance on information extraction tasks like named entity recognition and relation extraction.
This paper systematically studies how temporal metadata can be structurally embedded into named entity recognition (NER) models for historical texts. Experiments with absolute and relative temporal representations injected via early or late fusion mechanisms show that late fusion strategies yield more robust performance on French and German historical datasets.
This paper presents the results of HIPE-2026, the third edition of the HIPE evaluation series, which focuses on temporally grounded person-place relation extraction from multilingual historical documents in French, German, and English. Seventeen participating teams were evaluated on predictive accuracy, computational efficiency, and cross-domain generalization.
AAbAAC is a manually annotated corpus of 115 PubMed abstracts for autoimmunity information extraction, focusing on entities like autoimmune diseases and autoantibodies. The study demonstrates improved NER performance after fine-tuning on this corpus.
This paper investigates instruction finetuning of DeepSeek-R1-8B using LoRA and NEFTune for financial named-entity recognition, achieving a micro-F1 of 0.912 and outperforming several baseline models.
SMADE-IE is a sparse multi-agent framework for zero-shot information extraction that uses an Adaptive Mode Selector and Evidence-Driven Debate mechanism with Toulmin-style argumentation and Bayesian updates to outperform existing baselines on 9 benchmarks across NER, RE, and JERE tasks while improving token efficiency.
This paper introduces ChristBERT, a family of domain-specific RoBERTa-based language models for German clinical NLP, and evaluates three domain adaptation strategies (continued pre-training, pre-training from scratch, and vocabulary adaptation) on medical named entity recognition and text classification tasks, achieving state-of-the-art results.
This paper introduces BioConCal, a supervised scorer that uses inference-time panel and candidate features to rank biomedical entity candidates surfaced by LLM panels, significantly improving over raw agreement for curator triage.
This paper presents a specialty-specific medical language model for extracting information from clinical narratives about immune-mediated and infectious diseases, using a BiLSTM-CNN-Char architecture trained on a curated corpus of 371 case reports, achieving an F1 score of 0.89.
LELA is an LLM-based entity linking framework that combines zero-shot NER and entity disambiguation into an end-to-end Python library, validated across diverse settings.
Introduces ReDose, a dataset of 6,435 Reddit posts annotated for drug, dose, and effect entities, and benchmarks various models including BiomedBERT, Llama-3 70B, and GPT-4 for extraction.
The author used spaCy NER and Claude to extract place names from Shakespeare's works, then built an interactive map with MapLibre, OpenCage, and Stadia Maps, filtering places by play and displaying quotes.
UCCI proposes a calibration-first router for LLM cascades that uses isotonic regression to map token-level margin uncertainty to error probability, achieving a 31% cost reduction on a production NER workload while maintaining micro-F1=0.91 and reducing expected calibration error from 0.12 to 0.03.
This paper presents a method for comparing concordances of local grammars to optimize Named Entity Recognition for person names in Portuguese, achieving improved F-measure scores on the HAREM dataset.
Released en_legal_ner_ind_trf v0.1, an InLegalBERT model fine-tuned on 33,000 Indian Supreme Court judgments, achieving a 97.76% F1 score on case citations and significantly outperforming previous baselines.
ArXiv preprint identifies low information density as the root cause of NER performance collapse on noisy user-generated content and introduces the Window-Aware Optimization Module (WOM) that boosts F1 by up to 4.5% on WNUT2017.
DiZiNER is a framework that uses disagreement between multiple LLMs to refine task instructions for zero-shot named entity recognition, achieving state-of-the-art results on 14 out of 18 benchmarks and significantly reducing the performance gap between zero-shot and supervised systems.
Authors release Universal NER v2, a named-entity recognition paper presented at LREC 2026 that deliberately eschews modern scaling and tool-use trends.