Knowledge of syntax includes knowledge of rare, idiosyncratic constructions. LLMs must overcome frequency biases in order to master such constructions. In this study, I prompt GPT-3 to give acceptability judgments on the English-language Article + Adjective + Numeral + Noun construction (e.g., "a lovely five days"). I validate the prompt using the CoLA corpus of acceptability judgments and then zero in on the AANN construction. I compare GPT- 3's judgments to crowdsourced human judgments on a subset of sentences. GPT-3's judgments are broadly similar to human judgments and generally align with proposed constraints in the literature but, in some cases, GPT-3's judgments and human judgments diverge from the literature and from each other.
翻译:语言学知识包括稀有的、独特的建筑知识。LLMs必须克服频率偏差,才能掌握这些建筑。在本研究中,我促使GPT-3对英语条款+形容词+Numeral+Noun建筑(例如“可爱的五天 ” ) 作出可接受的判决。我利用COLA的可接受性判决汇编来验证及时性,而AANN建筑则零。我比较了GPT-3的判断与对一组判决的多方源人类判决。GPT-3的判断与人类判决大致相似,一般与文献中拟议的限制一致,但在某些情况下,GPT-3的判断和人类判决与文学和彼此不同。