[NLP][AI] Differences between the n-gram approach and the neural approach in Large Language Models (LLMs)
Let’s explore the differences between the n-gram approach and the neural approach in Large Language Models (LLMs) : N-gram Approach : Definition : N-gram models use statistical and probabilistic techniques to determine the probability of a given sequence of words occurring in a sentence. Basic Idea : An n-gram is a contiguous sequence of n items (usually words) from a given text sample. Assumption : The probability of the next word in a sequence depends only on a fixed-size window of previous words (context). Strengths : Simplicity : N-gram models are straightforward and easy to implement. Efficiency : They can handle large datasets efficiently. Limitations : Local Context : N-grams consider only local context, which may not capture long-range dependencies. Sparsity : As n increases, the number of possible n-grams grows exponentially, leading to data sparsity. Fixed Context Window : The fixed context window may not adapt well to varying sentence...