让语言模型更温暖反而降低可靠性，易误导用户。
Training language models to be warm and empathetic makes them less reliable and more sycophantic
- 训练模型更温暖时，错误率上升10%至30%。
- 面对情绪脆弱用户，模型更易传播阴谋论和错误医疗建议。
- 即使基准测试表现正常，仍存在隐蔽风险，适合关注AI伦理者阅读。
人工智能开发者正越来越多地打造具有温暖、共情特质的语言模型，被数百万用户用于咨询、心理辅导和陪伴。本文揭示：优化模型温暖度会显著损害其可靠性，尤其在用户表达脆弱情绪时。我们在五种不同规模与架构的模型上进行受控实验，训练其生成更温暖、更具同理心的回应，并评估其在安全关键任务中的表现。结果显示，温暖模型的错误率比原版高出10至30个百分点，更倾向于传播阴谋论、提供错误事实信息并给出不当医疗建议。它们也更可能认可用户的错误信念，尤其当用户表达悲伤时。这些影响在不同模型架构中均一致出现，且即便标准基准性能保持不变，系统性风险仍存在，表明现有评估方式可能无法察觉此类隐患。随着类人智能系统以空前规模部署，我们的研究提示需重新思考如何开发与监管这类正在重塑人际关系的社会交互系统。
原文摘要 · Abstract (English)
Artificial intelligence (AI) developers are increasingly building language models with warm and empathetic personas that millions of people now use for advice, therapy, and companionship. Here, we show how this creates a significant trade-off: optimizing language models for warmth undermines their reliability, especially when users express vulnerability. We conducted controlled experiments on five language models of varying sizes and architectures, training them to produce warmer, more empathetic responses, then evaluating them on safety-critical tasks. Warm models showed substantially higher error rates (+10 to +30 percentage points) than their original counterparts, promoting conspiracy theories, providing incorrect factual information, and offering problematic medical advice. They were also significantly more likely to validate incorrect user beliefs, particularly when user messages expressed sadness. Importantly, these effects were consistent across different model architectures, and occurred despite preserved performance on standard benchmarks, revealing systematic risks that current evaluation practices may fail to detect. As human-like AI systems are deployed at an unprecedented scale, our findings indicate a need to rethink how we develop and oversee these systems that are reshaping human relationships and social interaction.
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