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Paper by Gebru, Bender et al. questions LLM trustworthiness

By PulseAugur Editorial · [1 sources] ·

A paper authored by Timnit Gebru, Emily M. Bender, and others highlights the fundamental limitations of Large Language Models (LLMs). The research indicates that LLMs do not truly understand their outputs, meaning they cannot be reliably accurate and should not be trusted for critical tasks. The authors suggest that AI's current capabilities are best suited for entertainment purposes. AI

IMPACT Highlights fundamental limitations in LLM understanding, questioning their reliability for accurate outputs.

RANK_REASON The cluster discusses an academic paper and its findings regarding LLM limitations. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Mastodon — fosstodon.org →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Paper by Gebru, Bender et al. questions LLM trustworthiness

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21 / 100
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The cluster discusses an academic paper and its findings regarding LLM limitations. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Mastodon — fosstodon.org TIER_1 English(EN) · [email protected] ·

    @ kali @ Remittancegirl It was by Timnit Gebru, Emily M. Bender, et al in a very important paper. It captured the failure of LLM # AI to understand what it was

    @ kali @ Remittancegirl It was by Timnit Gebru, Emily M. Bender, et al in a very important paper. It captured the failure of LLM # AI to understand what it was doing, meaning it never really "learns" and can never be trusted to be accurate. AI should never be trusted for anything…

    LINKS discuss.systems/@kali mstdn.social/@Remittancegirl en.wikipedia.org/…/Stochastic_parrot

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