What is the primary goal of language modeling?

Question

BlackTom AI · Accepted Answer

When tackling the question about the primary goal of language modeling, it's important to align the options with the core definition of language modeling.
Option 1: 'To generate the most probable next word given a context of words.' This directly matches the standard objective of language models: predict the next token in a sequence based on preceding context, maximizing the likelihood of the continuation.
Option 2: 'To classify text into different categories.' This describes text classification or topic labeling, which is a downstream task that may use language representations, but it is not the fundamental aim of language modeling itself.
Option 3: 'To detect sentiment in text.' Sentiment analysis is a specific downstream task focused on emotional valence, not the foundational purpose of predicting word sequences in a language model.
To understand why Option 1 best fits, consider that language modeling evaluates how well a model can assign high probability to plausible word sequences, which is the core probabilistic objective behind training and evaluating such models.
The other options describe separate NLP tasks (classification and sentiment analysis) that rely on language understanding but are not the primary goal of a language model. They involve different modeling objectives or downstream applications rather than the intrinsic goal of predicting the next word in a sequence.

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What is the primary goal of language modeling?

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