arXiv AI June 10, 2026 By Dhruv Dixit

Detecting Speculative Language in Biomedical Texts using Recurrent Neural Tensor Networks

arXiv:2606. 10471v1 Announce Type: cross Abstract: In this investigation, we delve into the automated detection of speculative language within biomedical articles by utilizing distributed sentence representations and advanced deep learning techniques.

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arXiv Machine Learning

Aug 24

STS: Efficient Sparse Attention with Speculative Token Sparsity

arXiv:2605.15508v3 Announce Type: replace Abstract: The quadratic complexity of attention imposes severe memory and computational bottlenecks on Large Language Model (LLM) inference. This challenge i...

By Jiangnan Yu, Ceyu Xu, Yongji Wu, Yuan Xie

llms agents efficiency benchmarks

arXiv Machine Learning

Aug 24

MIL-BERT: Classification of Arbitrarily Large Text with Performance and Explanatory Guarantees

MIL-BERT is a neural network algorithm that classifies large texts by selecting relevant excerpts, inspired by multiple instance learning. It scales to samples with nearly 1 million tokens and has been evaluated on seven datasets, achieving state‑of‑the‑art results on three long‑text tasks such as political bias detection, trigger warning identification, and author demographic inference. The model also generalizes from weakly‑labeled text bags to accurately classify smaller instances.

By John Cadigan, Dayne Freitag, Eric Yeh

nlp benchmarks safety

arXiv Computation and Language

Sep 15

A Hybrid Hierarchical 1D-CNN-BiLSTM Framework for Extractive Summarization of Biomedical and Clinical Text

arXiv:2609.13481v1 Announce Type: new Abstract: Large language models have made abstractive summarization remarkably fluent, but generated summaries can hallucinate facts, posing serious risks in bio...

By Saad Bin Ather, Muhammad Saif, Ali Hassan Khan, Manzer Abbas, Hajra Waheed

llms rag nlp

arXiv Computation and Language

Sep 18

Fine-Tuning Models for Biomedical Relation Extraction

The paper introduces pre‑trained models for extracting variant‑phenotype relations from biomedical text, focusing on the SNPPhenA corpus. Fine‑tuning small BERT‑based models, especially DeBERTa, achieves performance close to the current state‑of‑the‑art. Moreover, careful fine‑tuning of Google’s Gemini Pro 1.0 surpasses existing benchmarks on both sentence‑level and abstract‑level relation extraction tasks.

By Claudiu Creanga, Liviu P. Dinu, Daniela Gifu

llms fine-tuning benchmarks

arXiv Computation and Language

Sep 17

Benchmarking Large Language Models for Biomedical Relation Extraction

arXiv:2609.19071v1 Announce Type: new Abstract: Extracting SNP-phenotype associations from biomedical literature is vital but challenging. We benchmarked diverse NLP models, including MLMs, hybrid ar...

By Claudiu Creanga, Teodor Marchitan, Liviu P. Dinu

llms nlp fine-tuning benchmarks

arXiv Machine Learning

2d ago

BioBigBird: A Sparse Attention Model for Long-Range Dependency Processing in Biomedical Text

arXiv:2610.11430v1 Announce Type: cross Abstract: While domain-specific Large Language Models (LLMs) have encoded vast biomedical knowledge, their limited context windows often hinder a deep understa...

By Roshan Balaji, Pavan Kumar S, Vasudev Gupta, Sreejith N, Keerthana Sridhar, Nirav Bhatt

llms nlp benchmarks

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STS: Efficient Sparse Attention with Speculative Token Sparsity

MIL-BERT: Classification of Arbitrarily Large Text with Performance and Explanatory Guarantees

A Hybrid Hierarchical 1D-CNN-BiLSTM Framework for Extractive Summarization of Biomedical and Clinical Text

Fine-Tuning Models for Biomedical Relation Extraction

Benchmarking Large Language Models for Biomedical Relation Extraction

BioBigBird: A Sparse Attention Model for Long-Range Dependency Processing in Biomedical Text