arXiv Machine Learning

Named Entity Recognition using Sliding Window Approach

The paper presents an inference-only pipeline that extends the frozen NER model MahaNER‑BERT to document‑level prediction using overlapping sliding windows, eliminating the need for retraining or architectural changes. The approach is evaluated on six document‑level corpora derived from the MahaNER test set, employing two repetition strategies (Normal Repeat and Random Repeat) at three length levels and various window configurations. Results show the model maintains a macro F1‑score of up to 0.8902 with minimal variation, outperforming non‑windowed methods by avoiding boundary‑fragmentation errors and achieving more stable document‑level performance.

arXiv Computation and Language

Sep 14

Doc2FRC: Length-Consistent Document-Level Machine Translation via Fixed-Range Chunking

Doc2FRC introduces Fixed-Range Chunking (FRC), a dynamic programming method that partitions documents into chunks of a predefined length interval, ensuring consistent length distributions during training and inference. This approach reduces train-test length mismatch, mitigates n-gram repetition, and improves translation quality for 7B LLMs compared to direct Doc2Doc fine-tuning. Experiments on IWSLT2017 and a new 10-language test set, GlobVDoc, demonstrate that FRC outperforms existing document-level machine translation methods and enhances out-of-distribution translation performance.

By Xiaotian Wang, Youyuan Lin, Zhan Shen, Hitomi Yanaka

llms rag nlp fine-tuning safety

arXiv Computation and Language

Sep 1

What Matters When Building Universal Multilingual Named Entity Recognition Models?

arXiv:2601.06347v3 Announce Type: replace Abstract: Recent progress in universal multilingual named entity recognition (NER) has been driven by multilingual transformer models, task-specific architec...

By Jonas Golde, Patrick Haller, Alan Akbik

llms nlp

arXiv Machine Learning

Jul 28

BERT-based Models vs. Large Language Models for Low-Resource Named Entity Recognition: A Comparative Study on Marathi

arXiv:2607. 23344v1 Announce Type: cross Abstract: Named Entity Recognition (NER) for low-resource languages such as Marathi remains a challenging task due to limited annotated resources and linguistic complexity.

By Hariom Ingle, Ronit Ghode, Ishwari Gondkar, Jidnyasa Harad, Raviraj Joshi

llms nlp fine-tuning

arXiv AI

Sep 23

Splitting Documents at Lower Cost: Multi-Split Boundary Decisions for LLM-Based Page Stream Segmentation

arXiv:2609.22620v1 Announce Type: new Abstract: Scanned mail, uploaded PDFs, and consolidated attachments often arrive as page streams that must be split into individual documents before downstream c...

By Nikhil Reddy Pottanigari, Sepideh Kharaghani, Saverio Vadacchino, Alejandro Posada, Ying Zhang

llms computer-vision

arXiv Machine Learning

Jun 2

GottBERT: a pure German Language Model

arXiv:2012. 02110v2 Announce Type: replace-cross Abstract: Pre-trained language models have significantly advanced natural language processing (NLP), especially with the introduction of BERT and its optimized version, RoBERTa.

By Raphael Scheible, Johann Frei, Fabian Thomczyk, Henry He, Patric Tippmann, Jochen Knaus, Victor Jaravine, Frank Kramer, Martin Boeker

llms nlp

arXiv AI

Aug 11

Enhancing Scientific Named Entity Recognition via Large Language Models: A Type-driven Multi-task Learning Approach

arXiv:2608. 08636v1 Announce Type: cross Abstract: Scientific named entity recognition (SciNER) plays a crucial role in information extraction and knowledge discovery from scientific texts.

By Tong Bao, Yi Zhao, Heng Zhang, Chengzhi Zhang

llms nlp

arXiv Machine Learning

5d ago

Language Specificity vs. Domain Diversity: Benchmarking Transformers for Bangla Medical NER

This study benchmarks transformer models for Bangla medical named entity recognition (NER), comparing BanglaBERT, multilingual BERT (mBERT), XLM‑RoBERTa, and GPT‑4o mini under zero‑shot and few‑shot prompting. Across a full test set of 3,179 samples, fine‑tuned XLM‑RoBERTa achieves a new state‑of‑the‑art F1‑score of 0.5959, while BanglaBERT lags with 0.4937, suggesting that domain diversity outweighs language specificity. The analysis shows high performance on Medicine and Specialist entities (F1 > 0.83) but lower accuracy on Symptoms (F1 0.4367), and demonstrates that fine‑tuned transformers outperform prompt‑only approaches by a factor of 3.76.

By Rakib Abdullah, Md. Maruful Islam Maruf

llms nlp fine-tuning benchmarks

arXiv AI

Jun 10

Attention Expansion: Enhancing Keyphrase Extraction from Long Documents with Attention-Augmented Contextualized Embeddings

arXiv:2606. 10716v1 Announce Type: cross Abstract: Pre-trained language models (PLMs) have achieved strong performance in keyphrase extraction (KPE), largely due to their ability to generate rich contextualized representations.

By Roberto Mart\'inez-Cruz, Alvaro J. L\'opez-L\'opez, Jos\'e Portela

llms rag efficiency benchmarks

Hugging Face Trending Papers

Jun 17

SenFlow: Inter-Sentence Flow Modeling for AI-Generated Text Detection in Hybrid Documents

Sentence-level AI-generated text detection (S-AGTD) for hybrid documents, where humans and LLMs co-author one text, faces two gaps: existing methods classify each sentence in isolation, discarding inter-sentence dependencies, and existing benchmarks omit the newest generation of generators. We construct MOSAIC, a benchmark of 16,000 hybrid documents over PubMed and XSum, generated by DeepSeek-V3.

llms benchmarks

arXiv AI

Sep 10

LitSeg: Narrative-Aware Document Segmentation for Literary RAG

arXiv:2605.27156v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by incorporating external knowledge, particularly for long-tail do...

By Ruikang Zhang, Zhanni Chen, Yiqiao Cai, Qi Su

llms rag computer-vision fine-tuning efficiency benchmarks

arXiv AI

Jun 29

A Study of Temporal Fusion Strategies for Named Entity Recognition in Historical Texts

arXiv:2606. 27881v1 Announce Type: cross Abstract: Temporal variation poses a unique challenge for named entity recognition (NER) in historical texts, where entities drift in surface form and salience across time.

By Emanuela Boros

llms nlp

arXiv AI

Sep 10

Climate-ModernBERT: Revisiting Corpus Composition for Domain-Adaptive Continued Pretraining

arXiv:2609.07798v1 Announce Type: cross Abstract: Natural Language Processing (NLP) in the climate domain requires models to process heterogeneous text sources, including scientific literature, polic...

By Yongan Yu, Shantam Raj, Jingwei Ni, Ario Saeid Vaghefi, Dominik Stammbach, Markus Leippold

nlp benchmarks

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