Summary of Bayesian Scaling Laws For In-context Learning, by Aryaman Arora et al.
Bayesian scaling laws for in-context learning
by Aryaman Arora, Dan Jurafsky, Christopher Potts, Noah D. Goodman
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Formal Languages and Automata Theory (cs.FL); 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 the connection between in-context learning (ICL) and language model accuracy. By viewing ICL as a Bayesian learner, researchers uncover novel scaling laws that outperform existing methods while providing interpretable terms for task priors, learning efficiency, and per-example probabilities. The findings are demonstrated through experiments with GPT-2 models of varying sizes and real-world instruction-tuned LLMs using capabilities benchmarks and a new many-shot jailbreaking dataset. The study sheds light on the limitations of post-training methods in increasing language model safety. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In-context learning helps language models do complex tasks without needing more training. Researchers found that the number of examples provided affects how well the model does its job. This paper shows why this is happening by looking at ICL like a Bayesian learner. This idea gives us new rules for making predictions, which are better than what we had before and easier to understand. We tested these rules with different-sized GPT-2 models and real-world language models. The results show that these rules work well in many situations. |
Keywords
» Artificial intelligence » Gpt » Language model » Scaling laws
