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Large language models (LLMs) have shown the emergent capability of in-context learning (ICL). One line of research has explained ICL as functionally performing gradient descent. In this paper, we introduce a new way of diagnosing whether ICL is funct
Externí odkaz:
http://arxiv.org/abs/2406.18501
Publikováno v:
Transactions of the Association for Computational Linguistics, Vol 8, Pp 125-140 (2020)
Learners that are exposed to the same training data might generalize differently due to differing inductive biases. In neural network models, inductive biases could in theory arise from any aspect of the model architecture. We investigate which archi
Externí odkaz:
https://doaj.org/article/ca442dfb7bd44ccf991dc7158480ae51
Autor:
Zhu, Xiaomeng, Frank, Robert
Discourse Entity (DE) recognition is the task of identifying novel and known entities introduced within a text. While previous work has found that large language models have basic, if imperfect, DE recognition abilities (Schuster and Linzen, 2022), i
Externí odkaz:
http://arxiv.org/abs/2403.06301
Language models are typically evaluated on their success at predicting the distribution of specific words in specific contexts. Yet linguistic knowledge also encodes relationships between contexts, allowing inferences between word distributions. We i
Externí odkaz:
http://arxiv.org/abs/2311.04900