Key Points

1. The paper investigates whether a new system called o1, developed by OpenAI and optimized for reasoning, still displays the autoregressive tendencies observed in previous large language models (LLMs) that were optimized for next-word prediction. Despite significant improvements over previous LLMs, o1 still exhibits the same qualitative behavioral patterns related to sensitivity to the probability of examples and tasks.

2. The authors use the teleological perspective to analyze the strengths and limitations of AI systems, focusing on the pressures that have shaped the systems. They argue that the primary training objective of autoregression (next-word prediction) has shaped the behavior of LLMs, causing them to be sensitive to the probability of the text they need to produce and the commonness of the task they are being asked to perform.

3. O1, while explicitly optimized for reasoning, is likely to have also undergone training for next-word prediction, which may contribute to its showing similar behavioral patterns as previous LLMs.

4. The paper presents evidence that o1 shows sensitivity to output probability, performing better on high-probability examples and requiring fewer "thinking tokens" in high-probability settings than in low-probability ones. This sensitivity is demonstrated across various tasks, such as decoding shift ciphers, Pig Latin, article swapping, and reversal.

5. O1 also exhibits some level of sensitivity to task frequency, performing better on rare task variants in some cases, but the authors note that this trend may be influenced by the difficulty of the examples.

6. The authors provide detailed analyses of o1's performance on specific tasks, showing that it tends to use more tokens for low-probability examples than high-probability ones, further supporting its sensitivity to output probability.

7. While o1 shows substantially less sensitivity to task frequency than previous LLMs, evidence suggests that it is still influenced by task frequency, especially in more challenging task variants, highlighting a qualitative behavioral pattern observed in previous systems.

8. The paper discusses potential reasons for o1's probability sensitivity, attributing it to the generation process in systems optimized for statistical prediction and the process of developing a chain of thought. The authors speculate that modeling enhancements involving components that do not involve probabilistic judgments may be needed to fully overcome these limitations.

9. The study concludes that o1's performance supports the teleological perspective. While it excels at reasoning tasks, it still displays behavioral signatures associated with being optimized for next-word prediction, and addressing these limitations may require incorporating model components that do not involve probabilistic judgments.

Additionally, the paper underscores that o1's operation details are not publicly available. This summary provides a comprehensive overview of the main findings and key points of the scientific article.

Summary

The study "Embers of Autoregression" investigates the limitations of large language models (LLMs) rooted in next-word prediction and compares the performance of a new system, o1 from OpenAI, which is optimized for reasoning. The researchers find that o1 substantially outperforms previous LLMs in many cases, with particularly large improvements on rare variants of common tasks. However, o1 still displays the same qualitative trends observed in previous systems. The study explores the quantitative and qualitative trends observed in o1, including its sensitivity to the probability of examples and tasks.

Performance Analysis
The analysis with o1 shows that it 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. This demonstrates that while optimizing a language model for reasoning can mitigate the limitations arising from next-word prediction, it may not fully overcome the model’s sensitivity to probability.

Analysis Approach
The researchers utilized a teleological approach to analyze the strengths and limitations of AI systems. They considered the pressures that have shaped LLMs, primarily the training objective of autoregression (next-word prediction). The paper presents evidence that o1, although explicitly optimized for reasoning, still displays behavioral patterns influenced by being optimized for next-word prediction, such as sensitivity to output probability and task frequency effects.

Task Sensitivity Evaluation
The study also evaluates o1's sensitivity to task frequency, concluding that while it shows substantially less sensitivity to task frequency than previous LLMs, there is still evidence of task frequency effects in some cases, especially in more challenging scenarios. The researchers highlight that although o1 performs impressively on tasks, it still qualitatively displays probability sensitivity and shows signs of next-word prediction optimization.

Teleological View
The findings support the view that developing a complete teleological analysis of an AI system requires consideration of all types of optimization applied to the system. Additionally, the study speculates that the process of generating text and developing a chain of thought in o1 may introduce biases toward high-probability scenarios, contributing to its probability sensitivity.

In conclusion, the study reveals that while o1 represents an impressive advance over previous LLMs, it still shows qualitative behavioral patterns influenced by next-word prediction optimization. The researchers suggest that overcoming these limitations may require the incorporation of model components that do not involve probabilistic judgments.

Reference: https://arxiv.org/abs/2410.01792