Summary of On Limitation Of Transformer For Learning Hmms, by Jiachen Hu et al.
On Limitation of Transformer for Learning HMMs
by Jiachen Hu, Qinghua Liu, Chi Jin
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 Transformers’ ability to learn basic sequential models like Hidden Markov Models (HMMs) is still unclear despite their success in various sequential modeling tasks. This paper investigates Transformers’ performance in learning HMMs and variants, comparing them to Recurrent Neural Networks (RNNs). The results show that Transformers consistently underperform RNNs in both training speed and testing accuracy across all tested HMM models, with some challenging instances where Transformers struggle to learn while RNNs can successfully do so. Additionally, the paper explores the relation between Transformer depth and longest sequence length it can effectively learn, based on HMM types and complexity. To address Transformers’ limitations, a variant of Chain-of-Thought (CoT), called block CoT, is demonstrated to help reduce evaluation error and learn longer sequences at the cost of increased training time. Finally, theoretical results prove the expressiveness of transformers in approximating HMMs with logarithmic depth. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Transformers are powerful AI models that can learn many things, like speech or text. But they’re not great at learning something called Hidden Markov Models (HMMs). This paper looks at how well Transformers do compared to another type of AI model called Recurrent Neural Networks (RNNs). The results show that RNNs are better than Transformers at doing this task. The paper also explores why this is the case and suggests a way to make Transformers better at learning HMMs. |
Keywords
» Artificial intelligence » Transformer
