Results 21 to 30 of about 17,696,681 (275)
Developing a language support model for mainstream primary school teachers [PDF]
, 2010 In the UK, speech and language therapists (SLTs) work with teachers to support children with language impairment (LI) in mainstream schools. Consultancy approaches are often used, where SLTs advise educational staff who then deliver language-learning ...
Ellis, Sue +10 more
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Synchronous models of language [PDF]
Proceedings of the 34th annual meeting on Association for Computational Linguistics -, 1996 In synchronous rewriting, the productions of two rewriting systems are paired and applied synchronously in the derivation of a pair of strings. We present a new synchronous rewriting system and argue that it can handle certain phenomena that are not covered by existing synchronous systems.
RAMBOW O., SATTA, GIORGIO
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TouchStone: Evaluating Vision-Language Models by Language Models
CoRR, 2023 Large vision-language models (LVLMs) have recently witnessed rapid advancements, exhibiting a remarkable capacity for perceiving, understanding, and processing visual information by connecting visual receptor with large language models (LLMs). However, current assessments mainly focus on recognizing and reasoning abilities, lacking direct evaluation of
Shuai Bai +8 more
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Modeling Language Evolution [PDF]
Foundations of Computational Mathematics, 2004 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Felipe Cucker +2 more
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Understanding Social Reasoning in Language Models with Language Models
Advances in Neural Information Processing Systems 36, 2023 As Large Language Models (LLMs) become increasingly integrated into our everyday lives, understanding their ability to comprehend human mental states becomes critical for ensuring effective interactions. However, despite the recent attempts to assess the Theory-of-Mind (ToM) reasoning capabilities of LLMs, the degree to which these models can align ...
Kanishk Gandhi +3 more
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Deriving Language Models from Masked Language Models
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2023 Masked language models (MLM) do not explicitly define a distribution over language, i.e., they are not language models per se. However, recent work has implicitly treated them as such for the purposes of generation and scoring. This paper studies methods for deriving explicit joint distributions from MLMs, focusing on distributions over two tokens ...
Lucas Torroba Hennigen, Yoon Kim
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Scene Text Recognition Based on Improved CRNN
Information, 2023 Text recognition is an important research topic in computer vision. Scene text, which refers to the text in real scenes, sometimes needs to meet the requirement of attracting attention, and there is the situation such as deformation.
Wenhua Yu +2 more
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What is the Role and Nature of Language and Language Development? [PDF]
, 2020 In this paper, the role and nature of language and language development will be discussed. Research and theory in second language acquisition has demonstrated that (i) language is an abstract, implicit and complex system.
Alessandro Benati, Benati, A
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Language models are not naysayers: an analysis of language models on negation benchmarks
Proceedings of the 12th Joint Conference on Lexical and Computational Semantics (*SEM 2023), 2023 Negation has been shown to be a major bottleneck for masked language models, such as BERT. However, whether this finding still holds for larger-sized auto-regressive language models (``LLMs'') has not been studied comprehensively. With the ever-increasing volume of research and applications of LLMs, we take a step back to evaluate the ability of ...
Thinh Hung Truong +3 more
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A General Technique to Train Language Models on Language Models [PDF]
Computational Linguistics, 2005 We show that under certain conditions, a language model can be trained on the basis of a second language model. The main instance of the technique trains a finite automaton on the basis of a probabilistic context-free grammar, such that the Kullback-Leibler distance between grammar and trained automaton is provably minimal.
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