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klotz: few-shot*

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1. Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?

   First, using the demonstrations significantly outperforms the no demonstrations method
   even with small k (k = 4), and performance drop
   from using gold labels to using random labels is
   consistently small across varying k, in the range of
   0.8–1.6%.7
   Interestingly, model performance does
   not increase much as k increases when k ≥ 8, both
   with gold labels and with random labels.

   2024-02-14 Tags: llm, in-context learning, few-shot, papers, arxiv by klotz
2. Language Models are Few-Shot Learners

   2024-02-14 Tags: llm, tom brown, neurips, few-shot, gpt-3 by klotz
3. Few-Shot Prompting | Prompt Engineering Guide

   Following the findings from Min et al. (2022)(opens in a new tab), here are a few more tips about demonstrations/exemplars when doing few-shot:

   2024-02-14 Tags: llm, few-shot, prompt by klotz
4. Leveraging GPT Models to Transform Natural Language to SQL Queries

   training GPT to query with few-shot prompting.

   2023-10-05 Tags: gpt, llm, sql, nlp, code generation, few-shot, prompt by klotz
5. Beyond chat-bots: the power of prompt-based GPT models for downstream NLP tasks

   2022-04-22 Tags: gpt-3, model, few-shot, nlp, example, github by klotz
6. Deep Few-shot Anomaly Detection. Harnessing a few labeled anomaly… | by Guansong Pang | Nov, 2020 | Towards Data Science

   2020-11-10 Tags: anomaly detection, few-shot, deep learning, reinforcement learning by klotz

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