README.md

Essentials of Large Language Models

Definition

• LM can be defined as probabilistic model that assigns probabilities to sequence of words or token in a given language.
• Goal is to capture the patterns and structure of the language and predict the likelihood of a particular sequence of words.
• Let's assume we have a vocabulary V that contains a sequence of words or tokens denoted as w1, w2,...wn, where n is the length of the sequence.
• The LM assign (p) to every possible sequence or order of word belonging to vocabulary(V).
• The probability of the entire dsequence can be expressed as follows:
  • p(w1, w2,...wn)

Example

• Assume we have V= {chase, the, cat, the, mouse}, and the following probabilities (p) are assigned:
  • p{chase, the, cat, the, mouse} = 0.0001
  • p{the, chase, cat, the, mouse} = 0.003
  • p{chase, the, mouse, the, cat} = 0.0021
  • p{the, cat, chase, the, mouse} = 0.02
  • p{the, mouse, chase, the, cat} = 0.01

NOTE: LMs must have external knowledge for them to be able to assign meanigful probabilities; therefore, they are trained. During this training process, the model learns to assign higher probabilities to words more likely to follow a given context. After training, the LM can generate text by sampling words based on these learned probabilities.

Prediction

• We can also predict a word in a given sequence.
• An LM estimates this probability by considering the conditional probabilities of each word given the previous words in the sequence.
• Using the chain rule of probability, the joint probability of the sequence can be decomposed as:
• p(w1, w2,...,wn) = p(w1) * p(w2|w1) * p(w3|w1,w2)...p(wn|w1,w2,...,wn-1)
• For example:
  • p(the, cat, chase, the, mouse) = p(the). p(cat|the). p(chase|the, cat). p(the|the, cat, chase) .p(mouse|the, cat, chase, the)

N-gram language model

• N-hram models are a type of probabilistic LM used in NLP and computational linguistics.
• These models are based on the idea that the probability of a word depends on the previous n - 1 words in the sequence. The term n-grams refers to a consecutive sequence of n items.
• For Example: consider the following sentence: I live language models
  • Unigram (1-gram): "I", "love", "language", "models"
  • Bigram (2-gram): "I love", "love language", "language models"
  • Trigram (3-gram): "I love language", "love language models"
  • 4-gram: "I love language models"

NOTE: More advanced LMs, sunh as recurrent NN, have been replaced by LLMs.

Algorithm

• Tokenization: Split the input text into individual words or tokens.
• N-gram generation: Create n-grams by forming sequence of n consecutive words from the tokenized text.
• Frequency counting: Count the occurences of each n-gram in the training corpus.
• Probability estimation: Calculate the conditional probability of each wor given to its previous n - 1 words using the frequency count.
• Smoothing: To handle unseen n-grams and avoid zero probabilities.
• Text generation: Start with an initial seed of n - 1 words, predict the next wor based on probabilities, and iteratively generate the next words to form a sequence.
• Repeat generation: Continue generating words until the desired length or a stopping condition is reached

Example:

import random

class NGramLanguageModel:
    def __init__(self, n):
        self.n = n
        self.ngrams = {}
        self.start_tokens = ['<start>'] * (n - 1)

    def train(self, corpus):
        for sentence in corpus:
            tokens = self.start_tokens + sentence.split() + ['<end>']
            for i in range(len(tokens) - self.n + 1):
                ngram = tuple(tokens[i:i + self.n])
                if ngram in self.ngrams:
                    self.ngrams[ngram] += 1
                else:
                    self.ngrams[ngram] = 1

    def generate_text(self, seed_text, length=10):
        seed_tokens = seed_text.split()
        padded_seed_text = self.start_tokens[-(self.n - 1 - len(seed_tokens)):] + seed_tokens
        generated_text = list(padded_seed_text)
        current_ngram = tuple(generated_text[-self.n + 1:])

        for _ in range(length):
            next_words = [ngram[-1] for ngram in self.ngrams.keys() if ngram[:-1] == current_ngram]
            if next_words:
                next_word = random.choice(next_words)
                generated_text.append(next_word)
                current_ngram = tuple(generated_text[-self.n + 1:])
            else:
                break

        return ' '.join(generated_text[len(self.start_tokens):])

# Toy corpus
toy_corpus = [
    "This is a simple example.",
    "The example demonstrates an n-gram language model.",
    "N-grams are used in natural language processing.",
    "This is a toy corpus for language modeling."
]

n = 3 # Change n-gram order here

# Example usage with seed text
model = NGramLanguageModel(n)  
model.train(toy_corpus)

seed_text = "This"  # Change seed text here
generated_text = model.generate_text(seed_text, length=3)
print("Seed text:", seed_text)
print("Generated text:", generated_text)

Large Language Models

• Refers to advance NLP models trained on massive amounts of textual data. These models are designed to understand and generate human-like text based on the input they receive.

LLMs vs LMs

Aspect	LLMs	LMs
Scale and Parameters	tens to hundreds of billions of parameters	Millions of parameters
Training Data	Trained on vast and diverse datasets from the internet	Can Can be trained on smaller, domain-specific datasets
Versatility	Higly versatile, excelling across various NLP tasks	Task specific, might require more fine-tuning
Computational Resources	Demands significant computational power and specialized hardware	More computational efficient, accessible on standard hardware
Use Cases	Complex language understanding, translation, summarization, creative writing	Specific tasks like sentiment analysis and named entity recognition

Next: ➡️ Types of LLMs