Large language models (LLMs) are a special class of pretrained language models (PLMs)
obtained by scaling model size, pretraining corpus and computation. LLMs, because of their …

Small data are often used in scientific and engineering research due to the presence of
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …

Предложена новая методика создания двухуровневой нейросетевой структуры,
предназначенной для решения ряда задач путем обучения отдельных нейросетей для …

In this paper, we experimentally evaluate the zero-shot performance of GPT-4 against prior
generations of GPT on the entire uniform bar examination (UBE), including not only the …

Deep generative models have unlocked another profound realm of human creativity. By
capturing and generalizing patterns within data, we have entered the epoch of all …

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a
driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted …

Federated learning is a privacy-preserving machine learning technique that trains models
across multiple devices holding local data samples without exchanging them. There are …

Semantic communication, regarded as the breakthrough beyond the Shannon paradigm,
aims at the successful transmission of semantic information conveyed by the source rather …

Transformer, an attention-based encoder–decoder model, has already revolutionized the
field of natural language processing (NLP). Inspired by such significant achievements, some …

The performance of Large Language Models (LLMs) in reasoning tasks depends heavily on
prompt design, with Chain-of-Thought (CoT) and self-consistency being critical methods that …