GPT-3能通过作家的图灵测试吗?

Q1 Arts and Humanities Journal of Cultural Analytics Pub Date : 2020-09-14 DOI:10.22148/001C.17212
Katherine Elkins, Jon Chun
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引用次数: 91

摘要

直到最近,自然语言生成领域依赖于形式化的语法系统、小规模的统计模型和冗长的启发式规则集。这种旧的技术相当有限和脆弱:它可以将语言重新组合成单词色拉诗,或者在狭窄的定义主题内与人类聊天。最近,非常大规模的统计语言模型极大地推动了该领域的发展,GPT-3只是其中一个例子。它可以内化语言的规则,而不需要明确的编程或规则。相反,就像人类儿童一样,GPT-3通过反复接触来学习语言,尽管规模要大得多。如果没有明确的规则,它有时会在最简单的语言任务上失败,但它也可以在模仿作家或哲学等更困难的任务上表现出色。
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Can GPT-3 Pass a Writer’s Turing Test?
Until recently the field of natural language generation relied upon formalized grammar systems, small-scale statistical models, and lengthy sets of heuristic rules. This older technology was fairly limited and brittle: it could remix language into word salad poems or chat with humans within narrowly defined topics. Recently, very large-scale statistical language models have dramatically advanced the field, and GPT-3 is just one example. It can internalize the rules of language without explicit programming or rules. Instead, much like a human child, GPT-3 learns language through repeated exposure, albeit on a much larger scale. Without explicit rules, it can sometimes fail at the simplest of linguistic tasks, but it can also excel at more difficult ones like imitating an author or waxing philosophical.
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来源期刊
Journal of Cultural Analytics
Journal of Cultural Analytics Arts and Humanities-Literature and Literary Theory
CiteScore
2.90
自引率
0.00%
发文量
9
审稿时长
10 weeks
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