large language models

Failure-mode-aware uncertainty intervention routing for large language models

LLMs Leak Training Data Beyond Verbatim Memorization: Extraction via Membership Decoding

The Membership Decoding method is a plug-and-play replacement for standard decoding that requires only black-box token probabilities, and a new token-level membership inference method is proposed by leveraging likelihood from reference models, shifting the generation from the original token distribution to the member token distribution.

How can public concerns shape eVTOL policy design? A knowledge graph-enhanced large language model (LLM) framework

A hybrid framework of large language models and transformer networks for multimodal stock movement prediction

LncPNdeep: A long non-coding RNA classifier based on large language model with peptide and nucleotide embedding.

Accurate classification of long non-coding RNAs (lncRNAs) is essential for transcriptome annotation and understanding gene regulation. Existing computational methods predominantly rely on nucleotide sequence features, frequently overlooking biologically relevant peptide signals encoded within lncRNAs. To overcome this limitation, we developed LncPNdeep, an integrative deep learning framework that combines nucleotide and peptide embeddings extracted via masked language models, specifically utilizing contextual representations from BigBird, Longformer, and ProtTrans. By fusing both features in a concatenated neural architecture, LncPNdeep robustly captures complex sequence relationships and improves discrimination between lncRNAs and coding RNAs. Benchmarking on the human transcriptome achieved state-of-the-art performance with 97.1% accuracy, surpassing established lncRNA classification tools and baseline machine learning models. LncPNdeep also demonstrated superior generalization ability across cross-species datasets, maintaining consistently high accuracy and F1 scores. Permutation analysis highlighted the pivotal role of peptide embeddings, especially Average Peptide Embedding, in model performance, while t-SNE visualizations confirmed that integrating multiple embeddings markedly enhances the separation of lncRNAs from coding RNAs. These results position LncPNdeep as a versatile and powerful tool for transcriptomic research, facilitating lncRNA discovery, biomarker identification, and comparative genomics. The model and instructions are freely available at https://github.com/yatoka233/LncPNdeep.

A bicycle traffic prediction framework using pretrained large language models

Integrating domain-specific knowledge graph with large language model for question-answering of construction laws and regulations: The case of China

Large language model-driven semantic clustering and zero-shot segmentation with structured query language-embedded learning from examples module 2 rules for analysing tax reform acceptance

A semi-supervised multi-stage pipeline for low-resource industrial condition monitoring by injecting domain knowledge via cross-modal multimodal large language model fine-tuning

MARS: Multi-Agent Rescheduling Framework with Large Language Models for Human-Centric Manufacturing

Research on wind turbine fault root cause localization method integrating large language models and causal hypergraphs

Profit-aware deployment of large language model-enabled inference chains in data centers