Comparatives, Quantifiers, Proportions: a Multi-Task Model for the Learning of Quantities from Vision (N18-1)
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| Challenge: | Comparatives, vague quantification, and proportional estimation are not learned at the same time nor governed by the same rules during language acquisition. |
| Approach: | They propose to combine sets comparison, vague quantification, and proportional estimation to learn them together from visual scenes. |
| Outcome: | The proposed model can generalize to unseen combinations of target/non-target objects. |
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| Challenge: | Generics are expressions used to communicate abstractions about categories . they allow for exceptions, and they are a powerful way to express knowledge about the world . |
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Some of Them Can be Guessed! Exploring the Effect of Linguistic Context in Predicting Quantifiers (P18-2)
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| Challenge: | cloze deletion test is a test that requires the learner to understand the context and vocabulary in order to identify the correct word. |
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| Challenge: | Current work suggests that language models deal poorly with quantifiers-they struggle to predict which quantifier is used in a given context and also perform poorly at generating appropriate continuations following logical quantifier. |
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| Challenge: | Recent work has found that multi-task training with a large number of diverse tasks can uniformly improve downstream performance on unseen target tasks. |
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Siwei Wu, King Zhu, Yu Bai, Yiming Liang, Yizhi Li, Haoning Wu, Jiaheng Liu, Ruibo Liu, Xingwei Qu, Xuxin Cheng, Ge Zhang, Wenhao Huang, Chenghua Lin
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| Challenge: | Recent popularity of generalised quantifiers and role in linguistics and logic raises the question of how they affect transformer-based language models (TLMs) |
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Generics are not quantificational: A new path from language models to semantic theory (2026.findings-acl)
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| Challenge: | Generic sentences express generalizations that tolerate exceptions without explicitly communicating information about quantities. |
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How Quantization Shapes Bias in Large Language Models (2026.eacl-long)
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| Challenge: | a systematic review of quantization's effects on model biases focuses on stereotypes, fairness, toxicity, and sentiment. |
| Approach: | They focus on weight and activation quantization strategies and examine their effects across bias types including stereotypes, fairness, toxicity, and sentiment. |
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