Summary of Position: Understanding Llms Requires More Than Statistical Generalization, by Patrik Reizinger et al.
Position: Understanding LLMs Requires More Than Statistical Generalization
by Patrik Reizinger, Szilvia Ujváry, Anna Mészáros, Anna Kerekes, Wieland Brendel, Ferenc Huszár
First submitted to arxiv on: 3 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: 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 paper explores why large language models (LLMs) generalize well by arguing that some desirable qualities are not a result of good statistical generalization and require separate theoretical explanation. The authors observe that probabilistic models with zero or near-zero Kullback-Leibler (KL) divergence apart can exhibit different behaviors, making them non-identifiable. This is demonstrated through three case studies: zero-shot rule extrapolation, in-context learning, and fine-tunability. The paper suggests promising research directions focusing on LLM-relevant generalization measures, transferability, and inductive biases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) are super smart! But have you ever wondered why they can do so many things without being explicitly trained for them? This paper helps answer that question by showing that some of the great things LLMS can do aren’t because they’re good at predicting what will happen. Instead, it’s because these models are very good at doing lots of different things and then choosing the right one. This is important to understand because it means we need to think about how to make LLMs even better, not just by making them smarter, but also by giving them a way to choose what to do in each situation. |
Keywords
» Artificial intelligence » Generalization » Transferability » Zero shot
