无智慧的知识：衡量大语言模型与预期影响之间的不一致
Knowledge without Wisdom: Measuring Misalignment between LLMs and Intended Impact
AI总结：
研究探讨了大语言模型在基准测试和下游任务中的对齐问题，发现模型行为与专家人类行为存在较大偏差，且常见预训练方法导致了显著的不一致。
摘要：
大语言模型在AI基准测试中表现优异，但这种表现并不保证在下游任务中有效。本研究对比了大语言模型在基准测试、下游任务以及这些任务预期影响上的对齐情况。我们评估了领先的大语言模型在验证难度较高的教学和学习任务中的表现。所有大语言模型在不同任务上的相互行为相关性高于其与专家人类行为在目标任务上的相关性。这些在大语言模型中共享的偏见与下游教学质量测量标准不一致，且常与学生学习成果的预期影响呈负相关。此外，我们发现多模型集成，无论是多数模型投票还是根据基准性能加权专家意见，进一步加剧了学习中的不一致。我们发现选择大语言模型和/或提示策略仅能可靠解释所有测量不一致误差的15%，且不一致误差的变化在不同大语言模型之间共享，表明共同预训练在这些任务中导致了大量不一致。我们展示了测量复杂任务对齐的稳健方法，并提供了大语言模型在高噪声环境中的实际应用独特见解。
英文摘要：
LLMs increasingly excel on AI benchmarks, but doing so does not guarantee validity for downstream tasks. This study contrasts LLM alignment on benchmarks, downstream tasks, and, importantly the intended impact of those tasks. We evaluate the performance of leading LLMs (i.e., generative pre-trained base models) on difficult-to-verify tasks of the teaching and learning of schoolchildren. Across all LLMs, inter-model behaviors on disparate tasks correlate higher than they do with expert human behaviors on target tasks. These biases shared across LLMs are poorly aligned with downstream measures of teaching quality and often negatively aligned with the intended impact of student learning outcomes. Further, we find multi-model ensembles, both unanimous model voting and expert-weighting by benchmark performance, further exacerbate misalignment with learning. We measure that selection of LLM and/or prompting strategy only reliably accounts for $15\%$ of all measured misalignment error and that variation in misalignment error is shared across LLMs, suggesting that common pretraining accounts for much of the misalignment in these tasks. We demonstrate methods for robustly measuring alignment of complex tasks and provide unique insights into practical applications of LLMs in high-noise contexts.
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