Language Model
IntermediateA model that assigns probabilities to sequences of tokens; often trained by next-token prediction.
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Why It Matters
Language models are crucial for advancing natural language processing, enabling applications that range from chatbots to automated content generation. Their ability to understand and generate human-like text has transformed industries such as customer service, education, and entertainment. As they become more sophisticated, they also raise important questions about ethics and bias in AI.
A language model is a probabilistic framework that assigns a probability distribution over sequences of tokens, typically words or subwords, in a given language. The mathematical foundation of language models often involves the use of n-grams, where the probability of a token is conditioned on the preceding n-1 tokens. More advanced models utilize neural networks, particularly recurrent neural networks (RNNs) and transformers, to capture long-range dependencies in text. The training objective commonly employed is next-token prediction, where the model learns to predict the likelihood of a token given its context. This approach is grounded in the principles of statistical language modeling and has significant implications for natural language processing tasks such as text generation, translation, and sentiment analysis. Language models can be categorized into autoregressive and masked types, with autoregressive models predicting the next token in a sequence and masked models predicting missing tokens in a context. The development of large-scale language models has led to significant advancements in the field, enabling applications that require understanding and generating human-like text.