Tootfinder

This https://arxiv.org/abs/2401.08495 has been replaced.
link: https://scholar.google.com/scholar?q=a

Large Language Models Portray Socially Subordinate Groups as More Homogeneous, Consistent with a Bias Observed in Humans
Large language models (LLMs) are becoming pervasive in everyday life, yet their propensity to reproduce biases inherited from training data remains a pressing concern. Prior investigations into bias in LLMs have focused on the association of social groups with stereotypical attributes. However, this is only one form of human bias such systems may reproduce. We investigate a new form of bias in LLMs that resembles a social psychological phenomenon where socially subordinate groups are perceived …

Microsoft launches Phi-3 Mini language model
Small enough to be deployed on a phone
https://www.computing.co.uk/news/4200785/microsoft-launches-phi-mini-language-model

Microsoft launches Phi-3 Mini language model
Microsoft has released a miniaturised AI model called Phi-3 Mini, the first in a family of lightweight models designed to bring AI capabilities to everyday devices.

This https://arxiv.org/abs/2305.07614 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

NevIR: Negation in Neural Information Retrieval
Negation is a common everyday phenomena and has been a consistent area of weakness for language models (LMs). Although the Information Retrieval (IR) community has adopted LMs as the backbone of modern IR architectures, there has been little to no research in understanding how negation impacts neural IR. We therefore construct a straightforward benchmark on this theme: asking IR models to rank two documents that differ only by negation. We show that the results vary widely according to the type…

Uncovering Name-Based Biases in Large Language Models Through Simulated Trust Game
Yumou Wei, Paulo F. Carvalho, John Stamper
https://arxiv.org/abs/2404.14682

Uncovering Name-Based Biases in Large Language Models Through Simulated Trust Game
Gender and race inferred from an individual's name are a notable source of stereotypes and biases that subtly influence social interactions. Abundant evidence from human experiments has revealed the preferential treatment that one receives when one's name suggests a predominant gender or race. As large language models acquire more capabilities and begin to support everyday applications, it becomes crucial to examine whether they manifest similar biases when encountering names in a complex socia…

Coimagining the Future of Voice Assistants with Cultural Sensitivity
Katie Seaborn, Yuto Sawa, Mizuki Watanabe
https://arxiv.org/abs/2403.17599 https://

Coimagining the Future of Voice Assistants with Cultural Sensitivity
Voice assistants (VAs) are becoming a feature of our everyday life. Yet, the user experience (UX) is often limited, leading to underuse, disengagement, and abandonment. Co-designing interactions for VAs with potential end-users can be useful. Crowdsourcing this process online and anonymously may add value. However, most work has been done in the English-speaking West on dialogue data sets. We must be sensitive to cultural differences in language, social interactions, and attitudes towards techn…

Methods for Matching English Language Addresses
Keshav Ramani, Daniel Borrajo
https://arxiv.org/abs/2403.12092 https://arxiv.org/pdf/…

Methods for Matching English Language Addresses
Addresses occupy a niche location within the landscape of textual data, due to the positional importance carried by every word, and the geographical scope it refers to. The task of matching addresses happens everyday and is present in various fields like mail redirection, entity resolution, etc. Our work defines, and formalizes a framework to generate matching and mismatching pairs of addresses in the English language, and use it to evaluate various methods to automatically perform address matc…

Instruction Tuning for Secure Code Generation
Jingxuan He, Mark Vero, Gabriela Krasnopolska, Martin Vechev
https://arxiv.org/abs/2402.09497 https://…

Instruction Tuning for Secure Code Generation
Modern language models (LMs) have gained widespread acceptance in everyday and professional contexts, particularly in programming. An essential procedure enabling this adoption is instruction tuning, which substantially enhances LMs' practical utility by training them to follow user instructions and human preferences. However, existing instruction tuning schemes overlook a crucial aspect: the security of generated code. As a result, even the state-of-the-art instruction-tuned LMs frequently pro…

This https://arxiv.org/abs/2403.06935 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Naming, Describing, and Quantifying Visual Objects in Humans and LLMs
While human speakers use a variety of different expressions when describing the same object in an image, giving rise to a distribution of plausible labels driven by pragmatic constraints, the extent to which current Vision \& Language Large Language Models (VLLMs) can mimic this crucial feature of language use is an open question. This applies to common, everyday objects, but it is particularly interesting for uncommon or novel objects for which a category label may be lacking or fuzzy. Further…

This https://arxiv.org/abs/2403.06935 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Naming, Describing, and Quantifying Visual Objects in Humans and LLMs
While human speakers use a variety of different expressions when describing the same object in an image, giving rise to a distribution of plausible labels driven by pragmatic constraints, the extent to which current Vision \& Language Large Language Models (VLLMs) can mimic this crucial feature of language use is an open question. This applies to common, everyday objects, but it is particularly interesting for uncommon or novel objects for which a category label may be lacking or fuzzy. Further…

This https://arxiv.org/abs/2307.12776 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

Assessing Large Language Models' ability to predict how humans balance self-interest and the interest of others
Generative artificial intelligence (AI) holds enormous potential to revolutionize decision-making processes, from everyday to high-stake scenarios. By leveraging generative AI, humans can benefit from data-driven insights and predictions, enhancing their ability to make informed decisions that consider a wide array of factors and potential outcomes. However, as many decisions carry social implications, for AI to be a reliable assistant for decision-making it is crucial that it is able to captur…

Naming, Describing, and Quantifying Visual Objects in Humans and LLMs
Alberto Testoni, Juell Sprott, Sandro Pezzelle
https://arxiv.org/abs/2403.06935 https…

Naming, Describing, and Quantifying Visual Objects in Humans and LLMs
While human speakers use a variety of different expressions when describing the same object in an image, giving rise to a distribution of plausible labels driven by pragmatic constraints, the extent to which current Vision \& Language Large Language Models (VLLMs) can mimic this crucial feature of language use is an open question. This applies to common, everyday objects, but it is particularly interesting for uncommon or novel objects for which a category label may be lacking or fuzzy. Further…

This https://arxiv.org/abs/2307.12776 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

Assessing Large Language Models' ability to predict how humans balance self-interest and the interest of others
Generative artificial intelligence (AI) holds enormous potential to revolutionize decision-making processes, from everyday to high-stake scenarios. By leveraging generative AI, humans can benefit from data-driven insights and predictions, enhancing their ability to make informed decisions that consider a wide array of factors and potential outcomes. However, as many decisions carry social implications, for AI to be a reliable assistant for decision-making it is crucial that it is able to captur…

This https://arxiv.org/abs/2404.00267 has been replaced.
link: https://scholar.google.com/scholar?q=a

Secret Keepers: The Impact of LLMs on Linguistic Markers of Personal Traits
Prior research has established associations between individuals' language usage and their personal traits; our linguistic patterns reveal information about our personalities, emotional states, and beliefs. However, with the increasing adoption of Large Language Models (LLMs) as writing assistants in everyday writing, a critical question emerges: are authors' linguistic patterns still predictive of their personal traits when LLMs are involved in the writing process? We investigate the impact of …