Introduction

Constrained Decoding is a powerful technique in NLP that ensures generated outputs adhere to specific rules or constraints. This is especially useful in tasks like code generation, structured text generation, and response formatting. With the help of Large Language Models (LLMs), constrained decoding enables controlled and accurate generation.

Why Use Constrained Decoding?

• Accuracy: Generate outputs that strictly follow predefined formats or rules.
• Safety: Prevent outputs that violate ethical or operational boundaries.
• Flexibility: Tailor model outputs to domain-specific requirements.

Methods for Constrained Decoding

1. Token Constraints: Restrict the model to choose from a specific set of tokens.
2. Beam Search with Constraints: Modify the beam search algorithm to enforce rules.
3. Post-Processing: Adjust outputs after generation to match constraints.
4. Custom Decoding Algorithms: Create custom decoding strategies for specific tasks.

Example: Constrained Decoding in Hugging Face

Here’s an example of generating text with specific constraints using the Hugging Face transformers library.

Task: Constrain Output to Specific Words

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model and tokenizer
model_name = "gpt2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Define input prompt
prompt = "The quick brown fox"

# Define token constraints (e.g., must include 'jumps' or 'runs')
allowed_tokens = [tokenizer.encode("jumps")[0], tokenizer.encode("runs")[0]]

# Custom constrained decoding function
def constrained_decoding(logits, allowed_tokens):
    mask = [i not in allowed_tokens for i in range(logits.shape[-1])]
    logits[:, mask] = -float("inf")
    return logits

# Generate constrained output
input_ids = tokenizer.encode(prompt, return_tensors="pt")
output = model.generate(
    input_ids, 
    max_length=20, 
    logits_processor=[lambda logits, _: constrained_decoding(logits, allowed_tokens)],
    do_sample=True
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print("Generated Text:", generated_text)

Applications of Constrained Decoding

• Code Generation: Ensure generated code adheres to syntax rules.
• Dialogue Systems: Generate responses aligned with conversational guidelines.
• Document Summarization: Produce summaries with specific formats or structures.
• Data-to-Text: Generate structured text (e.g., reports) from raw data.

Challenges

• Complex Constraints: Handling multiple overlapping constraints can increase computational overhead.
• Flexibility vs. Accuracy: Balancing creativity and adherence to constraints.
• Performance: Custom decoding can slow down generation compared to standard decoding.

Conclusion

Constrained Decoding with LLMs is a transformative technique that enhances the accuracy and reliability of generated outputs. By implementing constraints, you can tailor model behavior to meet the specific needs of your application.