Physical design method of MPSoC

Abstract

Harnessing logical reasoning ability is a comprehensive natural language understanding endeavor. With the release of Generative Pretrained Transformer 4 (GPT-4), highlighted as "advanced" at reasoning tasks, we are eager to learn the GPT-4 performance on various logical reasoning tasks. This report analyses multiple logical reasoning datasets, with popular benchmarks like LogiQA and ReClor, and newly-released datasets like AR-LSAT. We test the multi-choice reading comprehension and natural language inference tasks with benchmarks requiring logical reasoning. We further construct a logical reasoning out-ofdistribution dataset to investigate the robustness of ChatGPT and GPT-4. We also make a performance comparison between ChatGPT and GPT-4. Experiment results show that ChatGPT performs significantly better than the RoBERTa fine-tuning method on most logical reasoning benchmarks. With early access to the GPT-4 API we are able to conduct intense experiments on the GPT-4 model. The results show GPT-4 yields even higher performance on most logical reasoning datasets. Among benchmarks, ChatGPT and GPT-4 do relatively well on well-known datasets like LogiQA and ReClor. However, the performance drops significantly when handling newly released and out-of-distribution datasets. Logical reasoning remains challenging for ChatGPT and GPT-4, especially on outof-distribution and natural language inference datasets. We release the prompt-style logical reasoning datasets as a benchmark suite and name it LogiEval.

Design on College Sports Course Management System

“…The system from the sports course teaching reality, function implements information technology, real-time processing of sports information input to meet the practical needs. System put into operation so that the teaching workload is reduced greatly, improves the work efficiency, while provide decision basis for teaching management of school leadership, strengthen the monitoring of the teaching quality, but also for the teachers and students to provide a good service queries [8]. Make college sports teaching content selection and combination towards holistic, systematic and scientific development; improve the overall level and quality of the college sports course.…”

The Relationship Between Derogatory Terms and Their Essentialism Condition Focusing the Terms X dog in China

“…In the meantime, relevant researches, such as those conducted by Fugen Liu [2], Shuai Hong [3], and others, indicate that in ancient China, the power of 'X dog,' which consists of words with the structure of a simple noun or verb and the word "dog" and is frequently used as a derogatory term, gradually increased and became one of the most common derogatory terms. In contrast, in contemporary China, the functions of 'X dog' have shifted, and they are now widely used in daily life.…”