Deterministic Quoting

#machine-learning #ai #natural-language-processing

Deterministic Quoting

Definition

Deterministic Quoting is a technique used in machine learning, especially in natural language processing, to ensure that quotations from source materials are reproduced accurately and verbatim, rather than being "hallucinated" or made up by the model.

Explain Like I'm 5

Imagine you're playing a game of telephone. You have to pass on a message exactly as you heard it, without adding or changing anything. Deterministic quoting is like that - it makes sure the computer passes on quotes exactly as they were originally said or written.

Visualization

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Digging Deeper

Deterministic Quoting is an essential technique when training Natural Language Processing (NLP) models. These models are typically trained to generate human-like text based on the input data they receive. However, sometimes these models can "hallucinate", meaning they generate text that isn't present in the input data, especially when generating quotations. This can lead to misquotes and misinformation.

The purpose of deterministic quoting is to ensure that any quotes generated by the model are accurate and verbatim from the source material. This is achieved by implementing strict rules within the model's code that require it to reproduce quotes exactly as they appear in the input data.

Applications

Journalism: In automated article writing, deterministic quoting ensures accurate representation of people's statements.
Legal Technology: For automated document generation or analysis where accuracy of legal terms and citations is vital.
Academic Research: In automated literature reviews or meta-studies, maintaining fidelity of quotes from original studies is crucial.
Chatbots and Virtual Assistants: For providing accurate information based on sourced materials.