Summary of Many-shot In-context Learning, by Rishabh Agarwal et al.
Many-Shot In-Context Learning
by Rishabh Agarwal, Avi Singh, Lei M. Zhang, Bernd Bohnet, Luis Rosias, Stephanie Chan, Biao Zhang, Ankesh Anand, Zaheer Abbas, Azade Nova, John D. Co-Reyes, Eric Chu, Feryal Behbahani, Aleksandra Faust, Hugo Larochelle
First submitted to arxiv on: 17 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 This paper explores the capabilities of large language models (LLMs) in the “many-shot” regime, where hundreds or thousands of examples are provided at inference time. Building on previous work in few-shot in-context learning (ICL), the authors observe significant performance gains across various generative and discriminative tasks as the number of examples increases. To further mitigate limitations imposed by human-generated examples, two new settings are introduced: Reinforced ICL, which uses model-generated rationales, and Unsupervised ICL, which omits rationales altogether. Experimental results demonstrate that both methods can be effective in the many-shot regime, particularly on complex reasoning tasks. The authors also find that many-shot learning is more effective at overriding pretraining biases and can learn high-dimensional functions with numerical inputs. In addition, inference cost increases linearly with the number of examples provided. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how well large language models do when given lots of examples to work with. Currently, these models are great at learning from just a few examples, but they can get stuck if there aren’t enough examples available. To solve this problem, the researchers tried two new ways of using the models: one where the model generates its own explanations and another where it doesn’t use any explanations at all. The results show that both methods work well when given many examples to learn from. This is important because it means we can teach the models more efficiently and make them better at doing tasks that require a lot of understanding. |
Keywords
» Artificial intelligence » Few shot » Inference » Pretraining » Unsupervised
