Summary of Quantifying the Gain in Weak-to-strong Generalization, by Moses Charikar et al.
Quantifying the Gain in Weak-to-Strong Generalization
by Moses Charikar, Chirag Pabbaraju, Kirankumar Shiragur
First submitted to arxiv on: 24 May 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 Medium Difficulty summary: Recent advances in large language models have led to extraordinary capabilities, but evaluating and aligning them proves challenging for humans. The question arises: can guidance from weak models (like humans) direct the capabilities of strong models? Burns et al.’s 2023 work empirically demonstrates that when GPT-4 is finetuned using labels generated by GPT-2, it outperforms its weaker counterpart in a phenomenon called weak-to-strong generalization. This suggests that humans can effectively guide powerful language models, which has implications for natural language processing and machine learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: A new study found that super-smart computer programs (called large language models) are getting better at doing tasks that require human-like intelligence. But it’s hard to understand how these programs work well enough to decide what they’re good at or not. The researchers asked if humans can help guide these powerful computers, even though they’re much smarter than us. They found out that when a really smart computer (GPT-4) is taught using labels made by a slightly less smart computer (GPT-2), it does an even better job! This means that humans might be able to help super-smart computers learn and improve. |
Keywords
» Artificial intelligence » Generalization » Gpt » Machine learning » Natural language processing
