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.
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...
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.
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...
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.
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...
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...
arXiv:2608. 07208v1 Announce Type: cross Abstract: Existing measures of how much a text is about a concept read the surface of the text: dictionary word shares, topic proportions, embedding similarities.
Large Language Models suffer from high inference latency, especially when generating long chains of thought. Existing speculative decoding methods draft and verify tokens in parallel but ignore semantic equivalence, causing inefficient rejections. The proposed SemanticSpec framework verifies entire semantic sequences by probing internal hidden states, achieving up to 2.7× speedup on DeepSeekR1-32B and 2.1× on QwQ-32B while outperforming token‑level and sequence‑level baselines in both efficiency and effectiveness.
arXiv:2607. 08690v1 Announce Type: cross Abstract: Speculative decoding accelerates sampling from an autoregressive LLM by using a faster auxiliary model to draft tokens which are then verified in parallel by the LLM.
The paper introduces "question archaeology," an evaluation task that asks models to infer the single, authentic question that motivated a text. It presents a new dataset of commissioned texts paired with their original research questions and distractors, and evaluates both proprietary and open‑source LLMs. Results show newer models outperform older ones, with BERT-based models lagging, and current LLMs even surpassing human performance on this task.
arXiv:2606. 27550v1 Announce Type: cross Abstract: Multi-token prediction has been shown to increase data density during training, improve downstream text-generation quality, and serves as the defacto approach for self-speculative decoding.
arXiv:2606. 30675v1 Announce Type: cross Abstract: Early detection of dementia through speech analysis offers a non-invasive screening alternative, but capturing both acoustic and linguistic biomarkers remains challenging.