Summary of When a Language Model Is Optimized For Reasoning, Does It Still Show Embers Of Autoregression? An Analysis Of Openai O1, by R. Thomas Mccoy et al.
When a language model is optimized for reasoning, does it still show embers of autoregression? An analysis of OpenAI o1
by R. Thomas McCoy, Shunyu Yao, Dan Friedman, Mathew D. Hardy, Thomas L. Griffiths
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates whether the limitations of large language models (LLMs) persist with o1, a new system from OpenAI optimized for reasoning. The authors find that o1 outperforms previous LLMs in many cases, particularly on rare variants of common tasks. However, they also observe that o1 is sensitive to probability, performing better and requiring fewer “thinking tokens” in high-probability settings than low-probability ones. This study highlights the importance of optimizing language models for reasoning and shows that while it can mitigate some limitations, it might not fully overcome the model’s sensitivity to probability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at a new AI system called o1, which is designed to be better at thinking than previous AI systems. They find that o1 does much better on tricky tasks than the old systems do. However, they also see that o1 still has some of the same weaknesses as the old systems. This study shows that making an AI system better at thinking can help it get better results, but it doesn’t completely fix all its problems. |
Keywords
» Artificial intelligence » Probability
