The rabbit has dark gray fur, and its body color ranges from white, to black, and sometimes gray. It is a medium-sized, short haired rabbit, with an average weight of 1.3 kilograms and a height of 1.2 meters (4 feet). The new species, named the New World Rabbit (Oryctolagus cuniculus), is the first to be found in North America since 1872. Scientists recently discovered a new species of rabbit. This semantic interpretation, as I hope to show, can be formalized in a way that is actually computable using language models.Īs an illustration, consider the following prompt, which one might use as a way of generating descriptions of rabbits: But Boolean logic can also be interpreted in terms of meaning: "big and red" means the category of things that are both big and red. Boolean operators are usually understood in terms of truth values: "A and B" means that A and B are both true. The primary downside is an increase in the use of GPU memory.Īpart from introducing a new syntax, this project suggests a new interpretation of Boolean logic. In essence, this creates hybrid of a text generator and a programming language, making it easy to compose arrays of multiple prompt variants and experiment with new ways of manipulating the numerical outputs of language models. as opposed to entering a single text prompt that is fed directly into the model, one enters an expression that can incorporate the following operators: Operator This repo implements the rudiments of what I am hoping will become a broader set of techniques for controlling text generators. A related technique is calibration, which adjusts the probability distribution based on the output given a generic input. One proposed approach uses a secondary prompt to indicate forms of "bias" that are to be discouraged in the output. Some researchers have also developed new approaches to text generation that depart from the basic prompt-in, completion-out paradigm. The emergence of text generators has led to the practice of prompt engineering-that is, techniques (some automated, some manual) for designing better language model inputs. Sammet expressed over half a century ago: programming a computer in English.ĭesigning reliable prompts, however, is a complex matter. This development represents a step toward a dream the computer scientist Jean E. Applying this technique to particular problems requires constructing a prompt that induces the model to produce the desired output. By applying this procedure repeatedly, a model can generate a completion, meaning a continuation of the text that starts with a given prompt. These models are trained to perform a simple task: given the beginning of a text, the model tries to predict what word will come next. Neural text generators like the GPT models promise a general-purpose means of manipulating texts. PromptArray: A Prompting Language for Neural Text Generators
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