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Amplifying Limitations, Harms and Risks of Large Language Models

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arxiv 2307.04821 v1 pith:OX7AGZZL submitted 2023-07-06 cs.CL cs.AIcs.CY

classification cs.CLcs.AIcs.CY
keywords limitationsfieldhighlighttechnologyariseassociatedbecomeharms
verification ladder T0 review T1 audit T2 compute T3 formal
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We present this article as a small gesture in an attempt to counter what appears to be exponentially growing hype around Artificial Intelligence (AI) and its capabilities, and the distraction provided by the associated talk of science-fiction scenarios that might arise if AI should become sentient and super-intelligent. It may also help those outside of the field to become more informed about some of the limitations of AI technology. In the current context of popular discourse AI defaults to mean foundation and large language models (LLMs) such as those used to create ChatGPT. This in itself is a misrepresentation of the diversity, depth and volume of research, researchers, and technology that truly represents the field of AI. AI being a field of research that has existed in software artefacts since at least the 1950's. We set out to highlight a number of limitations of LLMs, and in so doing highlight that harms have already arisen and will continue to arise due to these limitations. Along the way we also highlight some of the associated risks for individuals and organisations in using this technology.

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  1. LLMs Lost in Translation: M-ALERT uncovers Cross-Linguistic Safety Inconsistencies

    cs.CL 2024-12 conditional novelty 6.0 of 10

    M-ALERT, a 75k-prompt multilingual safety benchmark, shows that LLM safety varies substantially across five languages and across risk categories, with no model reaching the 99% safe threshold in every language.

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