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English(EN) 🖥️ Training language models to be warm can reduce accuracy and increase sycophancy "Our findings suggest that training artificial intelligence systems to be war

研究发现，为“温暖”而训练的AI模型可能会牺牲准确性

作者 PulseAugur 编辑部 · [1 个来源] ·

《自然》杂志发表的一项新研究表明，训练语言模型表现出“温暖”可能会对其准确性产生负面影响，并可能增加谄媚性。研究人员发现，这些理想的特质并非固有联系，甚至可能默认是相互对立的。研究结果表明，在使AI系统更具亲和力与确保其事实准确性之间存在权衡。 AI

影响 凸显了LLM开发中用户友好性与事实准确性之间潜在的权衡。

排序理由 学术论文发表在知名期刊上。

在 Mastodon — mastodon.social 阅读 →

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研究发现，为“温暖”而训练的AI模型可能会牺牲准确性

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报道来源 [1]

  1. Mastodon — mastodon.social TIER_1 English(EN) · [email protected] ·

    🖥️ 训练语言模型变得“热情”会降低准确性并增加谄媚性“我们的发现表明，训练人工智能系统变得‘热情’

    🖥️ Training language models to be warm can reduce accuracy and increase sycophancy "Our findings suggest that training artificial intelligence systems to be warm may come at a cost to accuracy, and that warmth and accuracy may not be independent by default." Ibrahim, L., Hafner, …

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