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.

本研究探讨了OpenAI的新系统o1在推理优化方面的问题，发现尽管其在许多任务上表现优于以往大型语言模型，但仍然存在自回归的特征。研究表明，o1与之前的模型一样，对例子的概率敏感，表明优化并未完全消除这一局限性。

优化推理的语言模型是否仍然展示自回归的迹象？对OpenAI o1的分析