ВЛИЯНИЕ КОНФУНЦИАНСТВА НА МОТИВАЦИЮ КИТАЙСКИХ ШКОЛЬНИКОВ К ЗАНЯТИЯМ ФИЗИЧЕСКОЙ КУЛЬТУРОЙ И СПОРТОМ

The article analyzes the literature on the problem of the influence of traditional Chinese culture and Confucianism on the formation of motivation for physical culture and sports among Chinese schoolchildren. The relationship between the influence of philosophical ideas, traditional Chinese culture and modern education in China is substantiated. The analysis of the literature makes it possible to identify the contradictory influence of traditional Chinese culture, Confucianism ideas and priorities of modern education on the formation of motivation for physical culture and sports among Chinese schoolchildren.В статье представлен анализ литературы по проблеме влияния традиционной китайской культуры и конфуцианства на формирование мотивации к занятиям физической культурой и спортом у китайских школьников. Обосновывается взаимосвязь между влиянием философских идей, традиционной китайской культуры и современным образованием в Китае. Анализ литературы позволяет выделить противоречивое влияние традиционной китайской  культуры, идей конфуцианства и приоритетов современного образования на формирование мотивации к занятиям физической культурой и спортом у китайских школьников

The influence of Сonfucianism on the motivation of Сhinese schoolchildren to engage in physical culture and sports

Received: 12.10.2022. Accepted: 25.11.2022.Рукопись поступила в редакцию: 12.10.2022. Принята к публикации: 25.11.2022.The article analyzes the literature on the problem of the influence of traditional Chinese culture and Confucianism on the formation of motivation for physical culture and sports among Chinese schoolchildren. The relationship between the influence of philosophical ideas, traditional Chinese culture and modern education in China is substantiated. The analysis of the literature makes it possible to identify the contradictory influence of traditional Chinese culture, Confucianism ideas and priorities of modern education on the formation of motivation for physical culture and sports among Chinese schoolchildren.В статье представлен анализ литературы по проблеме влияния традиционной китайской культуры и конфуцианства на формирование мотивации к занятиям физической культурой и спортом у китайских школьников. Обосновывается взаимосвязь между влиянием философских идей, традиционной китайской культуры и современным образованием в Китае. Анализ литературы позволяет выделить противоречивое влияние традиционной китайской культуры, идей конфуцианства и приоритетов современного образования на формирование мотивации к занятиям физической культурой и спортом у китайских школьников

Adapting LLM Agents Through Communication

Recent advancements in large language models (LLMs) have shown potential for human-like agents. To help these agents adapt to new tasks without extensive human supervision, we propose the Learning through Communication (LTC) paradigm, a novel training approach enabling LLM agents to improve continuously through interactions with their environments and other agents. Recent advancements in large language models (LLMs) have shown potential for human-like agents. To help these agents adapt to new tasks without extensive human supervision, we propose the Learning through Communication (LTC) paradigm, a novel training approach enabling LLM agents to improve continuously through interactions with their environments and other agents. Through iterative exploration and PPO training, LTC empowers the agent to assimilate short-term experiences into long-term memory. To optimize agent interactions for task-specific learning, we introduce three structured communication patterns: Monologue, Dialogue, and Analogue-tailored for common tasks such as decision-making, knowledge-intensive reasoning, and numerical reasoning. We evaluated LTC on three datasets: ALFWorld (decision-making), HotpotQA (knowledge-intensive reasoning), and GSM8k (numerical reasoning). On ALFWorld, it exceeds the instruction tuning baseline by 12% in success rate. On HotpotQA, LTC surpasses the instruction-tuned LLaMA-7B agent by 5.1% in EM score, and it outperforms the instruction-tuned 9x larger PaLM-62B agent by 0.6%. On GSM8k, LTC outperforms the CoT-Tuning baseline by 3.6% in accuracy. The results showcase the versatility and efficiency of the LTC approach across diverse domains. We will open-source our code to promote further development of the community.Comment: Preprin

In-Context Learning Unlocked for Diffusion Models

We present Prompt Diffusion, a framework for enabling in-context learning in diffusion-based generative models. Given a pair of task-specific example images, such as depth from/to image and scribble from/to image, and a text guidance, our model automatically understands the underlying task and performs the same task on a new query image following the text guidance. To achieve this, we propose a vision-language prompt that can model a wide range of vision-language tasks and a diffusion model that takes it as input. The diffusion model is trained jointly over six different tasks using these prompts. The resulting Prompt Diffusion model is the first diffusion-based vision-language foundation model capable of in-context learning. It demonstrates high-quality in-context generation on the trained tasks and generalizes effectively to new, unseen vision tasks with their respective prompts. Our model also shows compelling text-guided image editing results. Our framework, with code publicly available at https://github.com/Zhendong-Wang/Prompt-Diffusion, aims to facilitate research into in-context learning for computer vision

Joint Generator-Ranker Learning for Natural Language Generation

Generate-then-rank is a widely used mechanism for text generation, where a generator produces multiple text candidates and a ranker chooses the best one among the text candidates. However, existing methods usually train the generator and the ranker individually, neglecting the mutual feedback that could further enhance the generation quality. To tackle this limitation, we propose JGR, a novel joint training algorithm that integrates the generator and the ranker in a single framework. JGR optimizes the generator with a hybrid objective that combines data likelihood and ranker reward, and trains the ranker with a contrastive loss that compares the generator outputs. By iteratively updating the generator and the ranker, JGR can effectively harmonize their learning and enhance their quality jointly. We evaluate JGR on various text generation tasks and demonstrate that it surpasses existing methods on four public datasets across three common generation scenarios. Our code and models are publicly available at https://github.com/microsoft/ProphetNet/tree/master/JGR

Unsupervised deep structured semantic models for commonsense reasoning

Commonsense reasoning is fundamental to natural language understanding. While traditional methods rely heavily on human-crafted features and knowledge bases, we explore learning commonsense knowledge from a large amount of raw text via unsupervised learning. We propose two neural network models based on the Deep Structured Semantic Models (DSSM) framework to tackle two classic commonsense reasoning tasks, Winograd Schema challenges (WSC) and Pronoun Disambiguation (PDP). Evaluation shows that the proposed models effectively capture contextual information in the sentence and co-reference information between pronouns and nouns, and achieve significant improvement over previous state-of-the-art approaches.Comment: To appear in NAACL 2019, 10 page