Zobrazeno 1 - 10
of 52
pro vyhledávání: '"Warstadt, Alex"'
Autor:
Choshen, Leshem, Cotterell, Ryan, Hu, Michael Y., Linzen, Tal, Mueller, Aaron, Ross, Candace, Warstadt, Alex, Wilcox, Ethan, Williams, Adina, Zhuang, Chengxu
After last year's successful BabyLM Challenge, the competition will be hosted again in 2024/2025. The overarching goals of the challenge remain the same; however, some of the competition rules will be different. The big changes for this year's compet
Externí odkaz:
http://arxiv.org/abs/2404.06214
Publikováno v:
LREC-Coling 2024, May 2024, Turin, Italy
The acquisition of grammar has been a central question to adjudicate between theories of language acquisition. In order to conduct faster, more reproducible, and larger-scale corpus studies on grammaticality in child-caregiver conversations, tools fo
Externí odkaz:
http://arxiv.org/abs/2403.14208
Autor:
Amariucai, Theodor, Warstadt, Alex
In contrast to children, language models (LMs) exhibit considerably inferior data efficiency when acquiring language. In this submission to the BabyLM Challenge (Warstadt et al., 2023), we test the hypothesis that this data efficiency gap is partly c
Externí odkaz:
http://arxiv.org/abs/2402.17936
Autor:
Warstadt, Alex, Parrish, Alicia, Liu, Haokun, Mohananey, Anhad, Peng, Wei, Wang, Sheng-Fu, Bowman, Samuel R.
Publikováno v:
Transactions of the Association for Computational Linguistics, Vol 8, Pp 377-392 (2020)
We introduce The Benchmark of Linguistic Minimal Pairs (BLiMP), 1
Externí odkaz:
https://doaj.org/article/790c378bb37a4835b5207578e84e0795
Publikováno v:
Transactions of the Association for Computational Linguistics, Vol 7, Pp 625-641 (2019)
This paper investigates the ability of artificial neural networks to judge the grammatical acceptability of a sentence, with the goal of testing their linguistic competence. We introduce the Corpus of Linguistic Acceptability (CoLA), a set of 10,657
Externí odkaz:
https://doaj.org/article/63af6d0d536b461091e6c964ad4cfee5
Autor:
Wolf, Lukas, Tuckute, Greta, Kotar, Klemen, Hosseini, Eghbal, Regev, Tamar, Wilcox, Ethan, Warstadt, Alex
Training on multiple modalities of input can augment the capabilities of a language model. Here, we ask whether such a training regime can improve the quality and efficiency of these systems as well. We focus on text--audio and introduce Whisbert, wh
Externí odkaz:
http://arxiv.org/abs/2312.02931
Autor:
Warstadt, Alex, Choshen, Leshem, Mueller, Aaron, Williams, Adina, Wilcox, Ethan, Zhuang, Chengxu
We present the call for papers for the BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus. This shared task is intended for participants with an interest in small scale language modeling, human language acquisition,
Externí odkaz:
http://arxiv.org/abs/2301.11796
We propose reconstruction probing, a new analysis method for contextualized representations based on reconstruction probabilities in masked language models (MLMs). This method relies on comparing the reconstruction probabilities of tokens in a given
Externí odkaz:
http://arxiv.org/abs/2212.10792
Language models are often trained on text alone, without additional grounding. There is debate as to how much of natural language semantics can be inferred from such a procedure. We prove that entailment judgments between sentences can be extracted f
Externí odkaz:
http://arxiv.org/abs/2209.12407
Autor:
Warstadt, Alex, Bowman, Samuel R.
Rapid progress in machine learning for natural language processing has the potential to transform debates about how humans learn language. However, the learning environments and biases of current artificial learners and humans diverge in ways that we
Externí odkaz:
http://arxiv.org/abs/2208.07998