relation-extraction

This paper investigates cross-lingual relation extraction for Romanian by translating the SemEval-2010 Task 8 benchmark and evaluating Gemma 4 under zero-shot, few-shot, and QLoRA fine-tuning, comparing with smaller encoder baselines.

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 presents DistilledGemma, a system for person-place relation extraction from multilingual historical newspaper articles using a three-stage knowledge distillation pipeline from a 26B Gemma teacher to a 2.3B student, achieving competitive accuracy and efficiency in the HIPE-2026 shared task.

Proposes ReaORE, a reasoning-guided framework for open relation extraction that progressively filters and predicts relations via coarse-to-fine reasoning, outperforming existing baselines on two 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of the HIPE evaluation series, which focuses on temporally grounded person-place relation extraction from multilingual historical documents in French, German, and English.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.

BCL is the first optimization framework that uses particle filtering with Bayesian updates to systematically refine label representations for information extraction tasks, showing consistent improvements over existing methods.

This paper investigates few-shot biomedical relation extraction using prompt-based learning with LLMs, comparing pairwise classification and joint generation approaches. The best model achieves micro-F1 of 0.44, outperforming previous few-shot results but remaining below supervised baselines, while macro-F1 surpasses the supervised baseline on rare relation types.

This paper introduces the task of extracting applicability conditions for therapeutic drug-disease relations from biomedical literature, creates a manually annotated dataset of triples, and proposes a LoRA-enhanced method that outperforms existing baselines.

This paper introduces variable-centered empirical graph extraction for psychology abstracts, constructing the EmpiriGraph-Psy benchmark dataset of 210 annotated abstracts and a staged LLM pipeline that achieves a macro-F1 of 0.74, outperforming direct extraction methods.

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.

GLiNER-Relex is a unified framework for joint named entity recognition and relation extraction that leverages a shared transformer encoder for zero-shot capabilities. The paper demonstrates competitive performance on standard benchmarks and releases the model as an open-source Python package.