arXiv Machine Learning July 28, 2026 By Zeyu Zhang, Xue Li, Iacer Calixto, Paul Groth, Sebastian Schelter

Beyond Scale and Generation: Understanding Language Model-based Entity Matching

Read the original on arXiv Machine Learning →

arXiv:2607. 24688v1 Announce Type: cross Abstract: Entity matching identifies records that refer to the same real-world entity.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI

Jul 29

IRIS: Reusable Identity Representations from Frozen LLMs for Entity Alignment

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Harnessing Structural Context for Entity Alignment Foundation Models

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Test-Time Augmentation for LLMs: When Input Diversity Beats Output Diversity at Matched Compute

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arXiv AI

Jul 1

Cross-lingual Relation Extraction with Large Language Models: Zero-Shot, Few-Shot, and Fine-Tuned Evaluation on Romanian

arXiv:2606. 31718v1 Announce Type: cross Abstract: Relation extraction (RE) for low-resource languages is typically constrained by the lack of annotated corpora.

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arXiv Machine Learning

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Cross-Architecture Steering Transfer in Language Models: A Systematic Empirical Study

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By Ayushi Agarwal

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arXiv Machine Learning

Jul 24

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators

arXiv:2607. 20465v1 Announce Type: new Abstract: The quality of training data fundamentally determines the capabilities of large language models (LLMs), yet no unified benchmark exists to measure how well LLMs, agents, and data-centric workflows actually prepare training data end to end.

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IRIS: Reusable Identity Representations from Frozen LLMs for Entity Alignment

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Test-Time Augmentation for LLMs: When Input Diversity Beats Output Diversity at Matched Compute

Cross-lingual Relation Extraction with Large Language Models: Zero-Shot, Few-Shot, and Fine-Tuned Evaluation on Romanian

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