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arXiv Detail & Related papers (2025-05-23T02:46:18Z)
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We show that language-specific ablation consistently boosts multilingual reasoning performance.<n>Compared to post-training, our training-free ablation achieves comparable or superior results with minimal computational overhead.
arXiv Detail & Related papers (2025-05-21T08:35:05Z)
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Linguistic diversity is a human attribute which, with the advance of generative AIs, is coming under threat. This paper examines the consequences of the variety selection bias imposed by technological applications and the vicious circle of preserving a variety that becomes dominant and standardized.
arXiv Detail & Related papers (2024-11-02T14:17:33Z)
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There are obvious benefits to integrating generative AI (artificial intelligence) into language learning and teaching. However, due to how AI systems under-stand human language, they lack the lived experience to be able to use language with the same social awareness as humans. There are built-in linguistic and cultural biases based on their training data which is mostly in English and predominantly from Western sources.
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arXiv Detail & Related papers (2023-10-19T14:50:51Z)
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multilingual language processing systems often exhibit a hardwired, yet usually involuntary and hidden representational preference towards certain languages. We show that biased technology is often the result of research and development methodologies that do not do justice to the complexity of the languages being represented. We present a new initiative that aims at reducing linguistic bias through both technological design and methodology.
arXiv Detail & Related papers (2023-07-25T10:53:20Z)
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Language Models as Inductive Reasoners [125.99461874008703]
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arXiv Detail & Related papers (2022-12-21T11:12:14Z)
CLSE: Corpus of Linguistically Significant Entities [58.29901964387952]
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arXiv Detail & Related papers (2022-10-14T02:35:19Z)
Norm Participation Grounds Language [16.726800816202033]
I propose a different, and more wide-ranging conception of how grounding should be understood: What grounds language is its normative nature. There are standards for doing things right, these standards are public and authoritative, while at the same time acceptance of authority can be disputed and negotiated. What grounds language, then, is the determined use that language users make of it, and what it is grounded in is the community of language users.
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arXiv Detail & Related papers (2021-08-06T23:49:18Z)
AM2iCo: Evaluating Word Meaning in Context across Low-ResourceLanguages with Adversarial Examples [51.048234591165155]
We present AM2iCo, Adversarial and Multilingual Meaning in Context. It aims to faithfully assess the ability of state-of-the-art (SotA) representation models to understand the identity of word meaning in cross-lingual contexts. Results reveal that current SotA pretrained encoders substantially lag behind human performance.
arXiv Detail & Related papers (2021-04-17T20:23:45Z)
ReferentialGym: A Nomenclature and Framework for Language Emergence & Grounding in (Visual) Referential Games [0.30458514384586394]
Natural languages are powerful tools wielded by human beings to communicate information and co-operate towards common goals. computational linguists have been researching the emergence of in artificial languages induced by language games. The AI community has started to investigate language emergence and grounding working towards better human-machine interfaces.
arXiv Detail & Related papers (2020-12-17T10:22:15Z)
Givenness Hierarchy Theoretic Cognitive Status Filtering [1.689482889925796]
Humans use pronouns due to implicit assumptions about the cognitive statuses their referents have in the minds of their conversational partners. We present two models of cognitive status: a rule-based Finite State Machine model and a Cognitive Status Filter. The models are demonstrated and evaluated using a silver-standard English subset of the OFAI Multimodal Task Description Corpus.
arXiv Detail & Related papers (2020-05-22T16:44:14Z)

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