Summary of Generalization V.s. Memorization: Tracing Language Models’ Capabilities Back to Pretraining Data, by Xinyi Wang et al.
Generalization v.s. Memorization: Tracing Language Models’ Capabilities Back to Pretraining Data
by Xinyi Wang, Antonis Antoniades, Yanai Elazar, Alfonso Amayuelas, Alon Albalak, Kexun Zhang, William Yang Wang
First submitted to arxiv on: 20 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 proposed task-gram language model effectively captures task-specific pretraining data frequency, allowing researchers to evaluate the memorization and generalization capabilities of large language models (LLMs) in different tasks. The study uses the Pythia models trained on the Pile dataset to evaluate four distinct tasks: machine translation, factual question answering, world knowledge understanding, and math reasoning. The results reveal varying levels of memorization, with stronger effects observed in simpler, knowledge-intensive tasks like factual question answering. In contrast, harder, reasoning-based tasks like machine translation and math reasoning show greater generalization, producing more novel outputs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) have incredible capabilities, but does this mean they truly generalize to new tasks or mostly memorize their training data? To find out, researchers introduced a new way of measuring how well an LLM remembers its training data. They also created a special kind of language model that can capture what’s important in each task. This helps us understand if an LLM is just repeating what it learned or coming up with new ideas. The study looked at four different tasks: translating words, answering questions, understanding the world, and doing math problems. The results show that some tasks require the LLM to remember more, while others need it to come up with new solutions. |
Keywords
* Artificial intelligence * Generalization * Language model * Pretraining * Question answering * Translation
