In "Embers of Autoregression" (McCoy et al., 2023), we showed that several large language models (LLMs) have some important limitations that are attributable to their origins in next-word prediction. Here we investigate whether these issues persist with o1, a new system from OpenAI that differs from previous LLMs in that it is optimized for reasoning. We find that o1 substantially outperforms previous LLMs in many cases, with particularly large improvements on rare variants of common tasks (e.g., forming acronyms from the second letter of each word in a list, rather than the first letter). Despite these quantitative improvements, however, o1 still displays the same qualitative trends that we observed in previous systems. Specifically, o1 - like previous LLMs - is sensitive to the probability of examples and tasks, performing better and requiring fewer "thinking tokens" in high-probability settings than in low-probability ones. These results show that optimizing a language model for reasoning can mitigate but might not fully overcome the language model's probability sensitivity.

本研究针对优化推理的语言模型o1，探讨其是否仍然存在自回归的不足。通过分析，我们发现o1在处理常见任务的稀有变体时表现显著提升，但仍然显示出对例子和任务概率的敏感性。这表明，尽管优化可以缓解语言模型的概率敏感性，但可能无法完全克服这一问题。

当一个语言模型被优化以进行推理时，它是否仍然展现出自回归的余烬？对OpenAI o1的分析