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pro vyhledávání: '"Wilcox, Ethan Gotlieb"'
Numerous previous studies have sought to determine to what extent language models, pretrained on natural language text, can serve as useful models of human cognition. In this paper, we are interested in the opposite question: whether we can directly
Externí odkaz:
http://arxiv.org/abs/2410.13086
We present a new perspective on how readers integrate context during real-time language comprehension. Our proposals build on surprisal theory, which posits that the processing effort of a linguistic unit (e.g., a word) is an affine function of its i
Externí odkaz:
http://arxiv.org/abs/2409.08160
Zipf (1935) posited that wordforms are optimized to minimize utterances' communicative costs. Under the assumption that cost is given by an utterance's length, he supported this claim by showing that words' lengths are inversely correlated with their
Externí odkaz:
http://arxiv.org/abs/2312.03897
A fundamental result in psycholinguistics is that less predictable words take a longer time to process. One theoretical explanation for this finding is Surprisal Theory (Hale, 2001; Levy, 2008), which quantifies a word's predictability as its surpris
Externí odkaz:
http://arxiv.org/abs/2307.03667
Publikováno v:
In Journal of Memory and Language October 2024 138
Publikováno v:
In Cognition August 2024 249
We present a targeted, scaled-up comparison of incremental processing in humans and neural language models by collecting by-word reaction time data for sixteen different syntactic test suites across a range of structural phenomena. Human reaction tim
Externí odkaz:
http://arxiv.org/abs/2106.03232
Human reading behavior is tuned to the statistics of natural language: the time it takes human subjects to read a word can be predicted from estimates of the word's probability in context. However, it remains an open question what computational archi
Externí odkaz:
http://arxiv.org/abs/2006.01912
Akademický článek
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Autor:
Mueller, Aaron, Warstadt, Alex, Wilcox, Ethan Gotlieb, Choshen, Leshem, Zhuang, Chengxu, Liu, Haokun
Evaluation data for the BabyLM Challenge. We filter for examples where each word has appeared in our strict-small dataset at least twice.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6a04464dcc13b64821b1f786c00b7d8c