[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):

1. 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 structures.
   • Common Use: Historically used for language modeling and machine translation.

2. Neural Approach (Neural Language Models):

   • Definition: Neural language models are based on neural networks, inspired by biological neural networks.
   • Basic Idea: These models use continuous representations (word embeddings) to make predictions.
   • Architecture: Common architectures include Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Transformer-based models.
   • Strengths:
     • Long Dependencies: Neural models can leverage longer word histories, especially with RNNs or Transformers.
     • Adaptability: They learn complex patterns and adapt to various sentence structures.
     • Parameter Sharing: Parameters can be shared across similar contexts.
   • Limitations:
     • Complexity: Neural models require more computational resources and training data.
     • Overfitting: Large neural models can overfit if not properly regularized.
   • Common Use: Widely used in modern LLMs like GPT-3, BERT, and XLNet.

3. Comparison:

   • N-gram models are simple and efficient but lack global context and struggle with sparsity.
   • Neural models capture long dependencies, adapt well, and handle complex patterns, but require more resources.
   • Hybrid Approaches: Some LLMs combine both approaches for better performance.