Chain of Thought Empowers Transformers to Solve Inherently Serial Problems
The study by Zhiyuan Li and colleagues demonstrates that the Chain of Thought approach enhances large language models' performance on arithmetic and symbolic reasoning tasks, enabling better serial computation capabilities.
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The paper titled "Chain of Thought Empowers Transformers to Solve Inherently Serial Problems" by Zhiyuan Li and colleagues explores the effectiveness of the Chain of Thought (CoT) approach in enhancing the performance of large language models (LLMs) on tasks requiring arithmetic and symbolic reasoning. The authors provide a theoretical framework that explains how CoT enables decoder-only transformers to perform inherently serial computations, a capability that is typically limited in low-depth transformers. Previous research indicated that constant-depth transformers with finite precision could only address problems in the complexity class TC^0 without CoT. This study tightens the expressiveness bounds for such transformers, showing they can only solve problems in AC^0 with constant-bit precision. However, by incorporating T steps of CoT, these transformers can tackle any problem solvable by boolean circuits of size T, significantly improving accuracy on challenging tasks like permutation group composition and circuit value problems. The findings suggest that CoT is a crucial mechanism for enhancing the computational capabilities of transformers, particularly in scenarios where parallel computation is insufficient.
- The Chain of Thought (CoT) method improves the accuracy of large language models on complex tasks.
- CoT enables transformers to perform serial computations, which are typically challenging for low-depth models.
- The study establishes tighter expressiveness bounds for constant-depth transformers.
- Incorporating CoT allows transformers to solve problems beyond the limitations of traditional approaches.
- Empirical results demonstrate significant performance improvements in tasks that are difficult for parallel computation.
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- Several commenters question the practical implications of the findings, suggesting that while theoretically interesting, they may not lead to real-world applications.
- There is a discussion about the relationship between transformers and established computational theories, such as the Universal Approximation Theorem.
- Some users express doubts about the novelty of the claims, arguing that the ability to solve problems with sufficient resources is not groundbreaking.
- Concerns are raised about the nature of reasoning in AI, with calls for models to not just generate language but to exhibit genuine understanding and logic.
- Comments highlight the need for further benchmarking and exploration of the CoT approach's effectiveness in practical scenarios.
25 comments> next paper: transformers can solve any problem but on some of them they may compute indefinitely and never provide an answer
> (and you cannot tell in advance which is which!!)
How would that be remarkable, when it is exactly what he Universal Approximation Theorem already states? Since transformers also use fully connected layers, none of this should really come as a surprise. But from glancing at the paper, they don't even mention it.
"What is the performance limit when scaling LLM inference? Sky's the limit.
We have mathematically proven that transformers can solve any problem, provided they are allowed to generate as many intermediate reasoning tokens as needed. Remarkably, constant depth is sufficient.
http://arxiv.org/abs/2402.12875 (ICLR 2024)"
Hello LLM, please solve this task: <task>
for iteration in range(10):
Hello LLM, please solve this task: <task>
Here is a possible solution: <last_reply>
Please look at it and see if you can improve it.
Then tell me your improved solution.This is also the case with plain and regular RNNs
"Running cellular automata and other programs on Claude 3 Opus."
Its one of the replies on this tweet.
But will they? I believe the frontier has moved to making them make sense instead of just making infinite language.
The infinite monkey problem is not solved yet
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