How deeply does Building LLM Applications with DSPy cover unlabeled data?

Defines input-only examples and their use for evaluation and optimization, without detailing the optimization procedure.

Unlabeled data

As explained in Building LLM Applications with DSPy

Data examples that lack target outputs or labels.

Unlabeled data consists of examples with inputs but no target outputs. For summarization, an example can contain the original document without a ground-truth summary. section 8.1.2

DSPy can use these examples for evaluation and optimization. This is useful when creating clear, meaningful summaries by hand—or generating them for human confirmation—is difficult and time-consuming. section 8.1.2

How Building LLM Applications with DSPy covers it

Cover of Building LLM Applications with DSPy

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Natural Language Processing in Action, Second EditionUnlabeled text is textual data that lacks human-provided category, entity, or other annotation labels.

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Related concepts

datakind of · Data is represented information, observations, measurements, or other values that can be stored, processed, transmitted, analyzed, or interpreted.
unsupervised learningused by · Unsupervised learning is a machine-learning approach that discovers patterns, structure, or representations in data without labeled target outputs.
labeled datacontrasts with · Labeled data consists of examples or observations paired with known target values, categories, or other task-relevant annotations.
transfer learningused by · Transfer learning is a machine-learning technique that reuses knowledge learned from one task, domain, or dataset to improve learning or performance on another.
self-supervised learningused by · Self-supervised learning is a machine-learning approach that derives training targets or supervisory signals from the data itself rather than relying on externally supplied labels.
autoencoderused by · An autoencoder is a neural network that encodes input data into an internal representation and decodes it to reconstruct the input or an approximation.

This explainer was generated by Manning's knowledge graph from the text of Building LLM Applications with DSPy. The section links point to the parts of the book it draws on.