The study reveals that under specific conditions, LLM behavior can be approximated via the behavior of a finite Markov chain. It also suggests that as the context window and vocabulary increase for LLMs, they seem to follow scaling laws similar to Markov chains. However, the article notes that traditional Markov chains are constrained to consider only a designated current state and a next state, while generative AI and LLMs can consider lengthy passages of text when generating a response, indicating that Markov chains may not fully capture the depth and flexibility of LLMs.
Key takeaways:
- The article discusses the potential of using Markov chains, a mathematical modeling technique, to gain insights about generative AI and large language models (LLMs).
- Markov chains involve a series of steps or states, with transitions from one state to another based on a statistical or probabilistic chance. This mirrors the process within generative AI and LLMs.
- Recent research has shown promising results in approximating LLM behavior via the behavior of a finite Markov chain under specific conditions, and it appears that as the context window and vocabulary increase for LLMs, they seem to follow scaling laws similar to Markov chains.
- However, a limitation of Markov chains is that they traditionally only consider the current state and the next state, whereas generative AI and LLMs can consider lengthy elements of input sequences when generating a response.