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Language as a bootstrap for compositional visual reasoning
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In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol 43, iss 43 (2021)
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Cetacean Translation Initiative: a roadmap to deciphering the communication of sperm whales ...
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Implicit Representations of Meaning in Neural Language Models ...
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Implicit Representations of Meaning in Neural Language Models ...
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How Do Neural Sequence Models Generalize? Local and Global Cues for Out-of-Distribution Prediction ...
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What Context Features Can Transformer Language Models Use? ...
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Quantifying Adaptability in Pre-trained Language Models with 500 Tasks ...
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Abstract:
When a neural language model (LM) is adapted to perform a new task, what aspects of the task predict the eventual performance of the model? In NLP, systematic features of LM generalization to individual examples are well characterized, but systematic aspects of LM adaptability to new tasks are not nearly as well understood. We present a large-scale empirical study of the features and limits of LM adaptability using a new benchmark, TaskBench500, built from 500 procedurally generated sequence modeling tasks. These tasks combine core aspects of language processing, including lexical semantics, sequence processing, memorization, logical reasoning, and world knowledge. Using TaskBench500, we evaluate three facets of adaptability, finding that: (1) adaptation procedures differ dramatically in their ability to memorize small datasets; (2) within a subset of task types, adaptation procedures exhibit compositional adaptability to complex tasks; and (3) failure to match training label distributions is explained by ... : 18 pages, 5 figures, 8 tables ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG
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URL: https://dx.doi.org/10.48550/arxiv.2112.03204 https://arxiv.org/abs/2112.03204
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One-Shot Lexicon Learning for Low-Resource Machine Translation ...
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What Context Features Can Transformer Language Models Use? ...
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The Low-Dimensional Linear Geometry of Contextualized Word Representations ...
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The Low-Dimensional Linear Geometry of Contextualized Word Representations ...
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A Benchmark for Systematic Generalization in Grounded Language Understanding ...
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