| Challenge: | a corpus of short texts in Mandarin is analyzed to examine the "coolness" hypothesis . quantified expressions are used to describe short texts, but are not as informative as English . |
| Approach: | They propose a corpus of Mandarin in which quantified expressions figure prominently. |
| Outcome: | The proposed corpus of short texts in Mandarin is compared with an English corpus. |
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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. |
| Approach: | They collect data from human participants and test various models in a local and a global context condition to examine the role of linguistic context in predicting quantifiers. |
| Outcome: | The proposed models outperform humans in a local and global context and are only slightly better in the latter. |
Quantifying Generalizations: Exploring the Divide Between Human and LLMs’ Sensitivity to Quantification (2024.acl-long)
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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 . |
| Approach: | They examine how large language models interpret generics to understand their meanings . they find that the presence of a generic sentence as context influences quantifiers based on the generalization . |
| Outcome: | The proposed models do not exhibit a strong sensitivity to quantification, the study finds . the results suggest that the presence of a generic sentence as context influences quantifiers . |
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. |
| Approach: | They compare generics and quantificational sentences to find out what quantifiers are . they argue that generics are not quantificationals, contrary to dominant views . |
| Outcome: | The proposed model recovers many semantic facts about quantifiers and their "quantificational counterparts". |
Not all quantifiers are equal: Probing Transformer-based language models’ understanding of generalised quantifiers (2023.emnlp-main)
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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) |
| Approach: | They propose to use textual entailment to assess the ability of TLMs to learn the meanings of generalised quantifiers by using a textual model-checking problem defined in a purely logical sense. |
| Outcome: | The proposed method allows the automatic construction of datasets with respect to which we can assess the ability of TLMs to learn the meanings of generalised quantifiers. |
How Does Quantization Affect Multilingual LLMs? (2024.findings-emnlp)
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Kelly Marchisio, Saurabh Dash, Hongyu Chen, Dennis Aumiller, Ahmet Üstün, Sara Hooker, Sebastian Ruder
| Challenge: | Quantization is widely used to improve inference speed and deployment of large language models. |
| Approach: | They conduct a thorough analysis of quantized multilingual LLMs . they find language disparately affected by quantization, non-Latin script languages worst . authors urge consideration of multilingual performance as evaluation criterion for efficient models . |
| Outcome: | The results show that quantization has harmful effects on human evaluation . language performance is disparately affected by quantization, the authors say . |
Generalized Quantifiers as a Source of Error in Multilingual NLU Benchmarks (2022.naacl-main)
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| Challenge: | Quantifiers are pervasive in NLU benchmarks and their occurrence at test time is associated with performance drops. |
| Approach: | They propose a generalized quantifier NLI task to quantify their contribution to the errors of NLU models. |
| Outcome: | The proposed model is based on a generalized quantifier theory and is compared with pre-trained models. |
Prepositions Matter in Quantifier Scope Disambiguation (2022.coling-1)
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Aleksander Leczkowski, Justyna Grudzińska, Manuel Vargas Guzmán, Aleksander Wawer, Aleksandra Siemieniuk
| Challenge: | Existing work on how to integrate world knowledge into a QSD model has been limited . |
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Rarely a problem? Language models exhibit inverse scaling in their predictions following few-type quantifiers (2023.findings-acl)
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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. |
| Approach: | They propose to use 960 English sentence stimuli to build 22 autoregressive transformer models of different sizes to test their performance on ‘few’-type quantifiers. |
| Outcome: | The proposed models perform poorly on ‘few’-type quantifiers, and the larger the model, the worse its performance. |
A Corpus of Encyclopedia Articles with Logical Forms (2020.lrec-1)
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| Challenge: | a corpus of annotated typed lambda calculus translations is described in this paper . typed Lambda Calculus expressions are intended to serve as a theory-neutral formal representation . |
| Approach: | They describe an annotated corpus of typed lambda calculus translations for 2,000 sentences in Simple English Wikipedia. |
| Outcome: | The annotated typed lambda calculus translations are used in a corpus of 2,000 sentences in Simple English Wikipedia. |
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. |
| Outcome: | The proposed method can reduce stereotypes and unfairness, but it tends to increase stereotypes in generative tasks. |