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Found 4 papers, 3 papers with code
Too Much Information: Keeping Training Simple for BabyLMs
This paper details the work of the University of Groningen for the BabyLM Challenge.
Leverage Points in Modality Shifts: Comparing Language-only and Multimodal Word Representations
Multimodal embeddings aim to enrich the semantic information in neural representations of language compared to text-only models.
Connecting degree and polarity: An artificial language learning study
We investigate a new linguistic generalization in pre-trained language models (taking BERT (Devlin et al., 2019) as a case study).
Transformers in the loop: Polarity in neural models of language
Establishing this allows us to more adequately evaluate the performance of language models and also to use language models to discover new insights into natural language grammar beyond existing linguistic theories.
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