Past year
All results
- All results
- Verbatim
Dec 11, 2023 · We show that meanings across languages manifest lower variability within semantic domains and greater variability between them.
Oct 2, 2024 · Our analysis provides compelling evidence that state-of-the-art embedding models struggle to capture domain-specific linguistic and semantic patterns.
Missing: Variability | Show results with:Variability
Apr 29, 2024 · In this paper, we present GrammarTransformer, an approach for transforming generated grammars in the context of meta-model-based language evolution.
Sep 27, 2024 · Our analysis provides compelling evidence that state- of-the-art embedding models struggle to capture domain-specific linguistic and semantic patterns, even ...
Missing: Variability | Show results with:Variability
Jun 21, 2024 · By expanding the model's linguistic context, it develops the ability to handle variations in style, vocabulary and tone. Despite limitations imposed by context ...
Missing: Variability | Show results with:Variability
Sep 7, 2024 · In this paper, we present an extension of the state-of-the-art XML Adaptive Hypermedia Model (XAHM), Object-Oriented XAHM (OO-XAHM) that supports the ...
Missing: Variability | Show results with:Variability
Dec 14, 2023 · Information-restricted neural language models reveal different brain regions' sensitivity to semantics, syntax, and context.
Missing: Variability | Show results with:Variability
May 22, 2024 · This article proposes a comprehensive strategy for training, evaluating, and optimizing domain-specific word2vec-based word embeddings, using social science ...
Dec 6, 2023 · Large language models can perform few-shot clas- sification through ICL with task-specific prompts consisting of a few labeled examples (Brown et al.,. 2020).
Jun 3, 2024 · We will dive into the details of fine-tuning, exploring how it can be leveraged to expand the models' “comfort zones” and hence uplift performance.
Missing: Variability | Show results with:Variability