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In recent years, LLMs have shown significant improvements in their overall performance. When they first became mainstream a couple of years before, they were already impressive with their seemingly human-like conversation abilities, but their reasoning always lacked. They were able to describe any sorting algorithm in the style of your favorite author; on the other hand, they weren't able to consistently perform addition. However, they improved significantly, and it's more and more difficult to find examples where they fail to reason. This created the belief that with enough scaling, LLMs will be able to learn general reasoning.
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Finding these optimization opportunities can itself be a significant undertaking. It requires end-to-end understanding of the spec to identify which behaviors are observable and which can safely be elided. Even then, whether a given optimization is actually spec-compliant is often unclear. Implementers must make judgment calls about which semantics they can relax without breaking compatibility. This puts enormous pressure on runtime teams to become spec experts just to achieve acceptable performance.
换言之，这种高信息密度、低确认的内容结构，本身便在制造新的认知风险。2025年2月，包括福布斯、康泰奈仕、洛杉矶时报在内的14家主流媒体机构，就对一家名为Cohere的公司提起诉讼，指责其在未经授权的情况下，批量复制了网站上的文章进行模型训练、生成新闻摘要，并且过程中容易滋生和放大“幻觉”风险，损害了媒体或出版商的声誉。
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