Language Models are Few-Shot Learners
Brown et al., 2020, Language Models are Few-Shot Learners (Advances in Neural Information Processing Systems 33 (NeurIPS 2020))
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2.4 Evaluation
- How were few-shot examples chosen when evaluating GPT-3?For each test example, \(K\) examples were drawn at random from the task’s training set (or the development set when no training set exists, as for LAMBADA), separated by newlines, sometimes with a natural language prompt; free-form answers used beam search (width 4).
- Brown et al., 2020, Language Models are Few-Shot Learners (Advances in Neural Information Processing Systems 33 (NeurIPS 2020)), §2.4
Source
- Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever and Dario Amodei. 2020. Language Models are Few-Shot Learners. In Advances in Neural Information Processing Systems 33 (NeurIPS 2020). https://papers.nips.cc/paper/2020/hash/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html