Abstract
With a handful of demonstration examples, large-scale language models demonstrate strong capability to perform various tasks by in-context learning from these examples, without any fine-tuning. We demonstrate that in-context learning performance can be highly unstable across samples of examples, indicating the idiosyncrasies of how language models acquire information. We formulate example selection for in-context learning as a sequential decision problem, and propose a reinforcement learning algorithm for identifying generalizable policies to select demonstration examples. For GPT-2, our learned policies demonstrate strong abilities of generalizing to unseen tasks in training, with a 5.8% improvement on average. Examples selected from our learned policies can even achieve a small improvement on GPT-3 Ada. However, the improvement diminishes on larger GPT-3 models, suggesting emerging capabilities of large language models.- Anthology ID:
- 2022.emnlp-main.622
- Volume:
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 9134–9148
- Language:
- URL:
- https://aclanthology.org/2022.emnlp-main.622
- DOI:
- Cite (ACL):
- Yiming Zhang, Shi Feng, and Chenhao Tan. 2022. Active Example Selection for In-Context Learning. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 9134–9148, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- Active Example Selection for In-Context Learning (Zhang et al., EMNLP 2022)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/2022.emnlp-main.622.pdf