Constructing the Signifier
Pre-GPT-3 models had much less capability to understand abstract descriptions of tasks due to their limited model of the world and human concepts. GPT-3’s impressive performance on 0-shot prompts indicates a new realm of possibilities for direct task specification.
A direct task specification is a 0-shot prompt which tells the model to perform a task that it already knows how to do using a signifier for the task. A signifier is a pattern which keys the intended behavior. It could be the name of the task, such as “translate”, a compound description, such as “rephrase this paragraph so that a 2nd grader can understand it, emphasizing real-world applications”, or purely contextual, such as the simple colon prompt from Figure 1. […]
In none of these cases does the signifier explain how to accomplish the task or provide examples of intended behavior; instead, it explicitly or implicitly calls functions which it assumes the language model has already learned.
Direct specifications can supervene on an infinity of implicit examples, like a closed-form expression on an infinite sequence, making them very powerful and compact. For instance, the phrase “translate French to English” supervenes on a list of mappings from all possible French phrases to English.
A Large Language Model, like a Person, has also learned behaviors for which it is less obvious how to construct a direct signifier. Task specification by demonstration (§4.3) and by proxy (§4.4) may be viable alternative strategies for eliciting those behaviors.
⇒ §4.3 ⇒ Task Specification by Memetic Proxy
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REYNOLDS, Laria and MCDONELL, Kyle, 2021. Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm. In: Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems. Online. New York, NY, USA: Association for Computing Machinery. 2021. p. 1–7. [Accessed 29 January 2023]. CHI EA ’21. ISBN 978-1-4503-8095-9. DOI 10.1145/3411763.3451760.