LogLLM: Leveraging Large Language Models for Improved Log-Based Anomaly Detection
Log-based anomaly detection has become essential to improve software system reliability by identifying problems from log data. However, traditional deep ...
Tag: Leveraging
Tokenformer: The Next Generation of Transformer Architecture Leveraging Tokenized Parameters for Seamless and Cost-Effective Scaling in AI Applications
Transformers have transformed artificial intelligence, delivering unmatched performance in NLP, computer vision, and multimodal data integration. These models excel at ...
Improving task scheduling in linguistic agents: Leveraging graph neural networks to improve task decomposition and decision making in large language models
Task planning in linguistic agents is gaining attention in LLM research, focusing on breaking down complex tasks into manageable subtasks ...
Leveraging smaller LLMs for enhanced recovery-augmented generation (RAG)
Call-3.2–1 B-Instruct and LanceDBAbstract: Retrieval Augmented Generation (RAG) combines large language models with external knowledge sources to produce more accurate ...
WaveletGPT: Leveraging Wavelet Theory for Faster LLM Training Across All Modalities
Large language models (LLMs) have revolutionized artificial intelligence and have impacted various scientific and engineering disciplines. The Transformer architecture, initially ...
Improving 3D reconstruction of sparse views with LM-Gaussian: leveraging large model priors for high-quality scene synthesis from limited images
Recent advances in sparse-view 3D reconstruction have focused on new view synthesis and scene representation techniques. Methods such as Neural ...
LongBench-Cite and LongCite-45k: Leveraging CoF (Coarse-to-Fine) methodology to improve long-context LLMs with fine-grained sentence-level citations to improve QC accuracy and reliability
Large language models (LLMs) have become essential tools for tasks such as question-answering (QA) and text summarization. These models are ...
Leveraging collaborative e-learning to support UDL
Key points: When we talk about learning methods, most of us think back to the time when we were considered ...
Magpie-Ultra dataset released: leveraging Llama 3.1 405B for diverse AI instruction-response pairs
Magpie-ultraA new dataset from the Argilla team has been released for supervised fine-tuning, which includes 50,000 instruction-response pairs. This synthetically ...
Leveraging AI to help special education teachers
Key points: technology has long been seen as holding the promise of making schools more effective and educators more efficient. ...
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