Toward Consistent World Models with Multi-Token Prediction and Latent Semantic Enhancement

Implement Multi-Token Prediction (MTP) with Latent Semantic Enhancement to train Large Language Models (LLMs). This method aims to improve internal world model consistency, reducing hallucination and enhancing complex reasoning abilities beyond traditional Next-Token Prediction.

advanced30 min5 steps

The play

Analyze Next-Token Prediction (NTP) Limitations
Review how standard LLM training (NTP) supervises one token at a time. Identify how this 'one-step-ahead' approach might limit global consistency and structured internal representations in LLMs.
Explore Multi-Token Prediction (MTP) as an Alternative
Investigate MTP as a training objective where the model predicts and is supervised on multiple tokens simultaneously. Understand how this encourages more structured and coherent internal representations compared to NTP.
Integrate Latent Semantic Enhancement
Study how Latent Semantic Enhancement can be combined with MTP to further improve the consistency and coherence of learned world models. Consider methods for extracting and incorporating latent semantic information during training.
Design a Training Experiment
Formulate a plan to implement and test an MTP-based training objective, potentially including Latent Semantic Enhancement, for a new or existing LLM. Define metrics to evaluate the consistency and robustness of the resulting world models.
Evaluate Model Consistency and Reasoning
Conduct experiments to compare LLMs trained with MTP (and LSE) against NTP-trained models. Focus evaluation on reducing issues like hallucination and improving complex reasoning abilities, demonstrating the impact on model reliability.

Starter code

import random

def generate_tokens_sequentially(start_text, num_tokens=5, vocab=None):
    """
    Simulates sequential token generation, conceptually illustrating multi-token output.
    In a real LLM, each prediction would be based on the extended context
    and a sophisticated model. This is a simplified, working example.
    """
    if vocab is None:
        vocab = ["the", "cat", "sat", "on", "mat", "quickly", "ran", "jumped", "sleeps", "."]

    generated = list(start_text.split())
    for _ in range(num_tokens):
        # In a real LLM, this would be model.predict_next_token(generated_so_far)
        # For this simulation, pick a random token from a small vocabulary
        next_token = random.choice(vocab)
        generated.append(next_token)
    return " ".join(generated)

# Example usage:
# Predict 3 tokens following "The dog"
print(generate_tokens_sequentially("The dog", num_tokens=3))

# Predict 4 tokens following "A bird flew"
print(generate_tokens_sequentially("A bird flew", num_tokens=4))

Source

Paperarxiv.org