In contrastive learning, a model gets both positive and negative samples as an input to differentiate between them. For word2vec for example, an algorithm that tries to model word similarity, the model gets as input that 'king' often has 'crown' as a context word (as a positive sample), whereas 'king' doesn't usually have 'beer', 'laptop', 'house' or 'bush' in its context (as negative samples). In this example, positive samples are drawn from real text and negative samples are just random words. Because 'king' and 'queen' might both co-occur with crown the word2vec model makes them also similar in its internal representation. This is how it can draw analogies like: 'king' - 'crown' + 'kippa' -> 'Jew' or 'rabbi'
spam fails_task lang_mismatch pii not_appropriate hate_speech sexual_content quality toxicity humor helpfulness creativity violence