Greedy Decoding is a text generation technique used in Natural Language Processing (NLP), particularly with models like GPT, T5, or other transformer-based models, to generate output token by token. It is one of the simplest decoding strategies and focuses on selecting the most likely next token at each step.

How Greedy Decoding Works

  1. Step-by-Step Token Generation:
    • At each generation step, the model predicts a probability distribution over all possible tokens in the vocabulary.
    • Greedy decoding selects the token with the highest probability (argmax) from the distribution.
  2. Sequential Process:
    • The chosen token is appended to the generated sequence.
    • The model then uses this updated sequence as input to predict the next token.
    • This process continues until:
      • A special end-of-sequence (EOS) token is generated.
      • A predefined maximum length is reached.

Advantages of Greedy Decoding

  1. Simplicity:
    • It is computationally efficient and straightforward to implement.
  2. Deterministic:
    • Given the same input, greedy decoding will always produce the same output, making it predictable.

Disadvantages of Greedy Decoding

  1. Suboptimal Results:
    • Greedy decoding can miss the globally optimal sequence because it focuses only on the most probable token at each step, without considering future tokens.
    • Example:
      • Model prediction for a sentence: “The cat is on the [mat, roof, bed].”
      • Greedy decoding might pick “mat” (highest probability), but “roof” could lead to a more coherent sequence later.
  2. Lack of Diversity:
    • It generates repetitive or overly generic outputs, especially in tasks like storytelling or dialogue generation.
  3. Poor Performance in Ambiguous Contexts:
    • If multiple plausible tokens have similar probabilities, greedy decoding may fail to explore alternative paths.

Comparison with Other Decoding Methods

| Method | Description | Advantages | Disadvantages | |———————|————————————————————-|————————————|————————————| | Greedy Decoding | Selects the token with the highest probability at each step | Fast and deterministic | Misses globally optimal solutions | | Beam Search | Explores multiple paths (beams) to find the most likely sequence | Balances exploration and exploitation | Computationally expensive | | Sampling | Selects tokens based on probability distribution (not just max) | Adds diversity to output | Can generate incoherent sequences | | Top-k Sampling | Samples from the top-k most probable tokens | Balances diversity and coherence | Still somewhat stochastic | | Top-p (Nucleus) | Samples tokens from a cumulative probability threshold | Highly flexible and dynamic | Requires careful tuning |

Applications of Greedy Decoding

  • Quick and Deterministic Generation:
    • Suitable for tasks where generating one correct answer is sufficient, such as:
      • Machine translation (e.g., Google Translate).
      • Question answering (e.g., FAQ bots).
      • Factual text generation.
  • Baselines for Comparison:
    • Greedy decoding is often used as a benchmark for evaluating the performance of more sophisticated decoding methods like beam search or sampling.

Example of Greedy Decoding

Input Prompt:

“Translate English to French: The cat is on the mat.”

Model Predictions (per step):

  1. Step 1: [“Le” (0.8), “Un” (0.1), “La” (0.05)] → Greedy Decoding selects “Le”.
  2. Step 2: [“chat” (0.9), “chien” (0.05), “oiseau” (0.02)] → Greedy Decoding selects “chat”.
  3. Step 3: [“est” (0.85), “sont” (0.1), “était” (0.05)] → Greedy Decoding selects “est”.
  4. Step 4: [“sur” (0.95), “dans” (0.02), “près” (0.01)] → Greedy Decoding selects “sur”.
  5. Step 5: [“le” (0.9), “un” (0.05), “la” (0.04)] → Greedy Decoding selects “le”.
  6. Step 6: [“tapis” (0.88), “sol” (0.05), “chaise” (0.02)] → Greedy Decoding selects “tapis”.

Output:

“Le chat est sur le tapis.”

When to Use Greedy Decoding

  • Use greedy decoding when:
    • Speed is critical, and the task does not require exploration of alternative outputs.
    • The task demands a single correct answer, and alternative outputs are unlikely to be beneficial (e.g., translation, extractive summarization).

Conclusion

Greedy decoding is a simple and efficient decoding strategy that works well for deterministic tasks but may fall short for tasks requiring creativity, diversity, or long-term planning. Understanding its strengths and limitations is essential for choosing the right decoding strategy based on the task’s requirements.