How Beam Search Operates on the Output of the Transformer
Introduction
The Transformer, a powerful neural network architecture, revolutionized natural language processing. It excels at tasks like machine translation, text summarization, and question answering. But how do we get the best possible output from a Transformer? This is where beam search comes into play. This article explores the interplay between these two technologies.
The Transformer: A Language Model
The Transformer uses a mechanism called attention to process sequences of data. Unlike traditional recurrent neural networks (RNNs), the Transformer doesn’t rely on sequential processing, allowing it to parallelize computations. It encodes the input sequence and then decodes it, generating a probability distribution over possible outputs.
Beam Search: Decoding the Probabilities
Beam search is a decoding algorithm that finds the most likely sequence of words given the output probabilities of the Transformer. It works by:
1. Initialization
- Start with a beam of size “k”.
- For each beam, initialize with the start-of-sequence token.
2. Iteration
- At each time step, predict the next token using the Transformer for each beam.
- Select the “k” most probable tokens and add them to the beam.
- This creates “k” possible continuations for each beam.
3. Termination
- Stop when the end-of-sequence token is generated or a maximum length is reached.
- The final sequence with the highest probability is selected as the output.
Illustrative Example
1. Transformer Output
Assume the Transformer predicts the following probabilities for the next token:
| Token | Probability |
|---|---|
| The | 0.3 |
| A | 0.2 |
| Cat | 0.1 |
| Dog | 0.4 |
2. Beam Search (k=2)
With a beam size of 2, the two most probable tokens, “Dog” and “The”, are chosen.
Time Step 1:
Beam 1: Dog (p=0.4) Beam 2: The (p=0.3)
Time Step 2:
The Transformer predicts probabilities for the next token given each beam.
Beam 1: Dog - The (p=0.2), Dog - Cat (p=0.1) Beam 2: The - Dog (p=0.4), The - Cat (p=0.3)
The two most probable continuations for each beam are chosen:
Beam 1: Dog - The (p=0.2) Beam 2: The - Dog (p=0.4)
Time Step 3:
This process continues until the end of sequence token is predicted or the maximum length is reached.
3. Output Selection
The final sequence with the highest probability is chosen as the output.
Benefits and Trade-offs
Benefits
- Improved Accuracy: Beam search explores multiple possible outputs, leading to higher accuracy compared to greedy decoding which only considers the single most probable token at each step.
- Diversity: Beam search can generate diverse outputs by considering alternative sequences.
Trade-offs
- Computational Complexity: Beam search is computationally expensive, especially with larger beam sizes.
- Determinism: The output is deterministic for a given input, unlike stochastic decoding methods like sampling.
Conclusion
Beam search is a vital decoding algorithm that works hand-in-hand with the Transformer. It enables us to extract the most likely and coherent sequences from the probabilistic output of the Transformer. This interplay empowers us to harness the power of these advanced language models for diverse applications.