May 18, 2023 · In this paper, we propose DynaICL, a recipe for efficient prompting with black-box generalist models that dynamically allocate in-context ...
May 18, 2023 · In this paper, we propose DYNAICL, a recipe for efficient prompting with black-box generalist models that dynamically allocate in-context.
We then append the corresponding number of in-context examples to the original prompt. The prompts are then fed into a generalist model to generate predictions.
May 18, 2023 · In this paper, we propose DynaICL, a recipe for efficient prompting with black-box generalist models that dynamically allocate in-context examples.
May 18, 2023 · The primary way of building AI applications is shifting from training specialist models to prompting generalist models.
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May 19, 2023 · "Efficient Prompting via Dynamic In-Context Learning" Cat: cs CL Link: https://t.co/A1UEzDgNx7.
Aug 11, 2024 · We propose a new method, Adversarial In-. Context Learning (adv-ICL1), to optimize prompts for in-context learning (ICL). Inspired.
Awesome resources for in-context learning and prompt engineering: Mastery of the LLMs such as ChatGPT, GPT-3, and FlanT5, with up-to-date and cutting-edge ...
Sep 9, 2024 · Efficient Prompting via Dynamic In-Context Learning (opens in a new tab) (May 2023); The Web Can Be Your Oyster for Improving Large Language ...
May 31, 2024 · The by far most popular approach to dynamic prompt engineering is RAG. The approach works well when trying to ingest context originating from ...
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