Is candidate sampling only for language models?

No. Large-catalog recommendation and other many-class tasks can use it.

Must sampling be uniform?

No. The distribution can be designed, but the objective and any corrections must match the sampling scheme.

Can sampled training loss replace full evaluation?

Not automatically. It measures the sampled objective; evaluation must reflect the intended use and allow a fair comparison.

AI Glossary / Generative AI and language / Intermediate

Candidate sampling

Definition

Candidate sampling is a training approach that evaluates a limited selection of output classes instead of all possible classes for every example. It typically includes the known target classes and sampled comparison classes, reducing work when the output vocabulary is very large.

What it means

Imagine training a model to predict one item from a catalog containing hundreds of thousands of items. Computing a score for every item on every training example can be expensive. Candidate sampling concentrates a training step on a smaller set of outputs while still teaching the model which observed targets should score well.

The sampled candidates belong to the output space. This differs from choosing a mini-batch of input examples. A training run can use both methods: sample some examples, then sample output classes for those examples.

The term also differs from candidate generation in a deployed recommendation service. Candidate generation retrieves items to consider for a user. Candidate sampling, in this entry, is a way to construct a training objective efficiently. It does not by itself specify how the deployed system searches its full catalog.

How it works

Start with an input context and its known target label or labels. Draw additional classes from a sampling distribution, such as a uniform or frequency-based distribution. Combine the targets and sampled classes according to the chosen training method, compute their scores, and use a loss to update model parameters.

The loss is crucial. Sampled softmax, noise-contrastive estimation and negative sampling are related candidate-based methods, but they do not all optimize the same expression or give scores with the same interpretation. Some methods correct scores using the sampling probabilities or expected counts. Treating them as interchangeable can change what the model learns.

A randomly selected class can coincide with a true target. The implementation must handle such accidental matches according to its objective rather than silently labeling a known positive as a negative. Duplicates, sampling without replacement and shared candidates across a batch also affect the bookkeeping.

The number and distribution of candidates influence computational cost and learning quality. Approximate training losses should be compared carefully: changing the candidate count can change the reported loss even without a meaningful improvement on the full task.

Three practical examples

A language model samples alternative vocabulary entries while training a large output classifier.
A catalog recommendation model trains with observed items and selected comparison items instead of scoring the entire catalog each time.
A system with many document tags samples comparison labels while retaining the known positive tags for each training example.

Think of it this way

Think of a trainee practicing identification with the correct object and a changing selection of alternatives. The trainee gains comparisons without reviewing every object in the collection during every exercise.

The analogy stops at practice design. A machine's loss needs precise probability and label handling. Easy alternatives alone may teach less than useful challenging alternatives, and sampled exercises do not automatically establish performance against the whole collection.

Worked example

Counting the scores, then checking the meaning

Suppose an invented classification task has 100,000 classes and one known target per example. A training step uses that target plus 199 distinct non-target candidates. It evaluates 200 class scores instead of 100,000, a 500-fold reduction in the number of output scores for that example.

This is not a claim of 500-fold faster training. Input encoding, memory transfers, sampling and other work still take time. Nor does a softmax probability of 0.8 among these 200 candidates automatically mean a probability of 0.8 among all 100,000 classes.

The training method must define the appropriate correction and loss. Evaluation should then measure the intended full-class or retrieval task under a stated protocol. The arithmetic illustrates the computational opportunity, not an accuracy or probability-calibration guarantee.

Illustrative case study

A fictional business, Maple Catalogue, changes its recommendation model from a small to a much larger sampled candidate set. The reported training loss increases, and the team initially interprets that as a regression.

Engineers discover that the model now competes against more alternatives in each step. They stop comparing the raw losses as if the objectives were unchanged. Instead, they evaluate both models on the same held-out users, catalog and ranking protocol, while recording serving cost and coverage of less frequent items.

The case demonstrates a measurement lesson: an easier sampled exercise can produce a lower loss without producing a better deployed recommendation system.

Common misconceptions and mistakes

Unselected classes are removed from the model forever. They may simply receive no direct output-class update in that particular sampled step.
Every unobserved item is a confirmed dislike. In recommendation data, lack of observation can have many causes.
Sampled softmax and negative sampling are identical algorithms. Their losses and score interpretations can differ.
Candidate sampling automatically saves the same proportion of total runtime as output calculations. Other costs remain and may dominate.

Business use

The method can make large-output training practical, but it creates decisions that should be recorded: candidate count, sampler, objective, treatment of accidental matches and evaluation protocol. These choices affect both resource use and the behavior learned from the data.

Business reviewers should ask whether the final evaluation includes realistic alternatives and rare classes. A model can look strong when compared only with easy random items and perform differently when the deployed service must distinguish among many plausible options.

Check your understanding

A training routine reports a high probability for the target after normalizing over 50 sampled alternatives. Can that number be reported directly as its probability across a million-item catalog?

Answer: No. The normalization covers a different candidate set, and the method's sampling corrections and output interpretation must be considered.

Related terms

Learn next

Compare full softmax with sampled softmax on a tiny class list where every score can be calculated. Then record what happens when the candidate set changes. This makes the distinction between computational savings, loss values and final ranking quality easier to inspect.

Sources

Sources: TensorFlow, Candidate Sampling Algorithms reference: https://www.tensorflow.org/extras/candidate_sampling.pdf TensorFlow, sampled softmax training loss: https://www.tensorflow.org/api_docs/python/tf/nn/sampled_softmax_loss Google Machine Learning Glossary, candidate sampling: https://developers.google.com/machine-learning/glossary#candidate-sampling

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