Greedy decoding is a simple and efficient method used in sequence generation tasks, where at each step, the algorithm selects the most probable token as the next output, based on the conditional probability given the previously generated tokens.
Greedy decoding is commonly applied in sequence generation tasks such as machine translation, text summarization, and speech recognition. It involves choosing the highest probability output at each step of the sequence generation, based on the conditional probability given the previous items.
How Greedy Decoding Works
Imagine a language model generating a sentence word by word. At each position where a new word is needed, the model calculates the probability of every possible word in its vocabulary appearing next, given the words already generated.
Here’s a step-by-step breakdown:
- Start: Begin with a starting token (e.g.,
<start>). - Calculate Probabilities: The model calculates the probability distribution over the entire vocabulary for the next token, conditioned on the tokens generated so far.
- Choose the Best: Select the token with the highest probability from the distribution.
- Append and Repeat: Add the selected token to the generated sequence and repeat steps 2-3 until an end-of-sequence token (e.g.,
<end>) is generated or a maximum length is reached.
This process is "greedy" because it always takes the seemingly best option at the current step without considering how that choice might impact future steps or the overall sequence probability.
Characteristics and Considerations
- Simplicity: It's straightforward to implement.
- Speed: It's computationally inexpensive compared to other decoding strategies like beam search because it only explores one path.
- Local Optimality: It finds the locally optimal token at each step.
- Global Sub-optimality: This local focus can lead to a globally sub-optimal sequence. A sequence with a slightly lower probability token early on might lead to a much higher cumulative probability later.
Example:
Suppose a model needs to complete the sequence "The dog...".
- Step 1: Model calculates probabilities for the next word given "The dog".
- It might find:
- "is" - probability 0.95
- "ran" - probability 0.04
- "ate" - probability 0.01
- Greedy decoding picks "is" (0.95) because it's the highest probability word at this step.
- The sequence becomes "The dog is".
Now, consider the next step for "The dog is". The model might find:
- "sleeping" - probability 0.80
- "happy" - probability 0.10
- "small" - probability 0.05
Greedy picks "sleeping" (0.80). The final sequence is "The dog is sleeping" with a combined (simplified) probability (0.95 * 0.80 = 0.76).
However, an alternative path could have been: "The dog ran" (prob 0.04).
From "The dog ran", the model might find:
- "quickly" - probability 0.99
- "away" - probability 0.01
Picking "quickly" (0.99) gives "The dog ran quickly" with a combined (simplified) probability (0.04 * 0.99 = 0.0396). In this simple case, the greedy choice led to a higher probability sequence.
Now consider a scenario where the sequence "The dog is" could lead to "The dog is very very very large" (many steps with moderate probability words), while "The dog ran" could lead to "The dog ran home" (few steps with high probability words). Greedy might get stuck in a less optimal long path.
Analogy Table:
| Concept | Greedy Decoding Approach | Analogy |
|---|---|---|
| Goal | Generate a sequence of tokens. | Building a sentence. |
| At Each Step | Choose the single most probable token. | Picking the best word now. |
| Decision Making | Based only on the current step. | Short-sighted decision. |
| Outcome Quality | Simple, Fast, but not always the best overall sequence. | Quick sentence, but maybe not the most natural or accurate. |
Applications
Greedy decoding is a baseline or default strategy in many sequence generation systems due to its speed and simplicity. While it might not always produce the highest quality output compared to beam search or other more complex methods, it's often sufficient for initial experiments or applications where inference speed is critical.
For instance, in real-time speech recognition, a fast decoding method like greedy decoding or a limited beam search is crucial for providing immediate results. In machine translation, it can produce readable but sometimes less fluent translations compared to beam search.
Limitations
The primary limitation is that making locally optimal choices does not guarantee global optimality. This can lead to:
- Repetitions: The model might get stuck repeating the same phrase.
- Bland Text: It might favor common, high-probability phrases that lack creativity or specificity.
- Sub-optimal Translations/Summaries: It might miss out on better sequences of words simply because the first few words of that sequence had slightly lower initial probabilities.
Despite its limitations, greedy decoding remains a fundamental concept and a useful starting point in understanding more advanced sequence decoding strategies.
