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arXiv 2304.02468cs.CL
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ChatGPT与语言模型演进的比较分析

Comparative Analysis of CHATGPT and the evolution of language models

Siemens Technology(西门子科技);California State University, San Bernardino(加州州立大学圣贝纳迪诺分校)

Oluwatosin Ogundare, Gustavo Quiros Araya

AI总结:

本文分析了ChatGPT在自然语言处理中的表现,使用SQ分数比较其与主流算法在翻译、摘要、问答和生成任务上的性能,并提出验证策略以促进LLMs的安全大规模应用。

摘要:

自ChatGPT出现以来,人们对大型语言模型(LLMs)的兴趣急剧增加,社会对其在自然语言处理(NLP)任务中表现出的便捷性给予了积极的反响。然而,ChatGPT的成功在于它无缝地弥合了语言生成与知识模型之间的鸿沟。在某些情况下,它为在知识领域内复制人类直觉的框架提供了轶事证据。本文重点介绍了NLP领域的主流思想,包括机器翻译、机器摘要、问答和语言生成,并使用自发质量(SQ)分数比较了ChatGPT与这些类别中主要算法的性能。文章简要提出了一种验证ChatGPT论点和结果的策略,作为LLMs安全、大规模应用的示例。

英文摘要:

Interest in Large Language Models (LLMs) has increased drastically since the emergence of ChatGPT and the outstanding positive societal response to the ease with which it performs tasks in Natural Language Processing (NLP). The triumph of ChatGPT, however, is how it seamlessly bridges the divide between language generation and knowledge models. In some cases, it provides anecdotal evidence of a framework for replicating human intuition over a knowledge domain. This paper highlights the prevailing ideas in NLP, including machine translation, machine summarization, question-answering, and language generation, and compares the performance of ChatGPT with the major algorithms in each of these categories using the Spontaneous Quality (SQ) score. A strategy for validating the arguments and results of ChatGPT is presented summarily as an example of safe, large-scale adoption of LLMs.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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