Summary of Large Language Models As Markov Chains, by Oussama Zekri et al.
Large Language Models as Markov Chains
by Oussama Zekri, Ambroise Odonnat, Abdelhakim Benechehab, Linus Bleistein, Nicolas Boullé, Ievgen Redko
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
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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 abstract proposes a novel theoretical analysis of large language models’ (LLMs’) generalization capabilities, drawing an equivalence between autoregressive transformer-based language models and Markov chains. This approach enables the study of LLMs’ multi-step inference mechanism from first principles. The authors relate their findings to the pathological behavior observed with LLMs, such as repetitions and incoherent replies at high temperatures. They derive pre-training and in-context learning generalization bounds for LLMs under realistic data and model assumptions. Experimental results on recent Llama and Gemma models demonstrate that the theory accurately captures their behavior. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are very good at understanding and generating text. But have you ever wondered why they’re so good? A team of researchers wanted to figure out how these models work, especially when they start making weird responses or repeating themselves. They used a new way of thinking about language models, comparing them to a type of math problem called Markov chains. This helped them understand how the models make predictions and decisions. The results showed that this approach can help predict when a model might start behaving strangely. The researchers also tested their ideas on real language models and found that they accurately predicted what these models would do. |
Keywords
» Artificial intelligence » Autoregressive » Generalization » Inference » Llama » Transformer
