arXiv AI August 18, 2026 By Khajesh Sapram, Srivardhani Raju, Kishore Konda

Domain-Specific Text Embedding Models for Entity Resolution

arXiv:2608. 16161v1 Announce Type: cross Abstract: General-purpose text embedding models are designed to capture semantic similarity but are not optimised for distinguishing entity records that represent the same real-world business or person.

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arXiv Computation and Language

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LentEx: Generalizable Latent Entity Extraction via Synthetic Data and Instruction-Tuned LLMs

LentEx is a new framework for latent entity extraction that uses synthetic data generation and instruction fine‑tuning to train smaller, efficient large language models. By creating diverse, contextually rich synthetic examples through a template‑based approach, LentEx overcomes the lack of labeled datasets and achieves strong performance, surpassing state‑of‑the‑art models on the MTEB Clustering Benchmark. The method also generalizes well to unseen domains, making it useful for tasks such as retrieval‑augmented generation, customer persona analysis, and knowledge graph enrichment.

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llms rag nlp fine-tuning benchmarks safety

arXiv Computation and Language

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Mitigating Entity Type Confusion in Cross-Domain NER via Multidimensional Quantification and Reasoning Enhancement

arXiv:2609.24357v1 Announce Type: new Abstract: Cross-domain Named Entity Recognition (CD-NER) aims to transfer the rich knowledge in the source domain to the target domain. Recent studies adopting d...

By Jingyu Wang, Shijie Wu, Fusheng Jin

llms nlp benchmarks

arXiv AI

Sep 23

Semantic Candidate-Job Matching: A Comparative Evaluation of Dense Embedding Models in Hybrid Retrieval

arXiv:2609.23307v1 Announce Type: cross Abstract: This paper presents a comparative evaluation of dense embedding models for semantic candidate-job matching in high-volume staffing workflows. Incomin...

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Hugging Face Trending Papers

Sep 21

Mitigating Entity Type Confusion in Cross-Domain NER via Multidimensional Quantification and Reasoning Enhancement

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llms nlp benchmarks

Hugging Face Trending Papers

Jul 23

Achieving Text-based Person Retrieval with Any Granularity

Text-based person retrieval faces a critical but under-explored challenge: the inherent uncertainty of query granularity in real-world scenarios. This paper introduces a new paradigm, Text-based Person Retrieval with Any Granularity, and provides a systematic solution.

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arXiv Machine Learning

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

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

By Zeyu Zhang, Xue Li, Iacer Calixto, Paul Groth, Sebastian Schelter

llms rag fine-tuning benchmarks

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