Constraining LLM outputs with grammar definitions

#translation #ai

I’ve been thinking about how to improve LLM performance for Bible translation, especially for low-resource languages. The current approaches aren’t great - LLMs struggle with the nuances of these languages and often produce poor translations. This is a big problem since we need translations that respect both cultural and linguistic aspects.

Combining Old and New Approaches

Rule-Based Grammars + Context-Aware LLMs

The first thing I tried was combining traditional rule-based grammars with modern LLMs. Rule-based grammars are pretty rigid on their own, but they give us a solid structure to work with. When you mix this with an LLM’s ability to understand context, you get something much more useful. The grammar provides guardrails while the LLM handles the nuanced language understanding.

Using LLMs to Build Grammars

Here’s an interesting twist - we can actually use LLMs to help write grammars for low-resource languages. Instead of just using them as translators, we’re using them to build the linguistic frameworks we need. I think this could be a game-changer for working with lesser-known languages.

How It All Works Together

The cool part is seeing how these old-school grammar rules work with modern LLM capabilities. What used to be seen as outdated becomes really powerful when combined with the way LLMs process context and meaning.

Grammar-Constrained Decoding

I’ve been looking into using context-free grammars (CFGs) like GBNF to control LLM outputs. This approach, called grammar-constrained decoding (GCD), works particularly well for structured content.

What does this actually look like? The best example I’ve come across so far is the GBNF grammar approach found in the llama.cpp library. They give the following example of a formal ‘grammar’ for specifying that the only outputs that an LLM can give must be Japanese characters:

# A probably incorrect grammar for Japanese
root ::= jp-char+ ([ \t\n] jp-char+)*
jp-char ::= hiragana | katakana | punctuation | cjk
hiragana ::= [ぁ-ゟ]
katakana ::= [ァ-ヿ]
punctuation ::= [、-〾]
cjk ::= [一-鿿]

Benefits and Challenges

Research has revealed several key advantages of using grammar constraints:

Improved Accuracy: Studies show dramatically reduced error rates when using grammar-based constraints, especially for longer sequences
Efficient Learning: LLMs can learn from fewer examples - in some cases, needing only about 16.5% of the usual training samples
Domain Knowledge Integration: Grammars provide a way to inject external knowledge and domain-specific constraints into the generation process

However, there are some challenges to consider:

Distribution Distortion: Some grammar constraint techniques can distort the LLM’s natural distribution, potentially affecting output quality
Implementation Complexity: Effective grammar constraints often require specialized frameworks or algorithms

The Road Ahead

This hybrid approach isn’t just a theoretical exercise; it’s a practical solution to a real-world problem. By harnessing the strengths of both rule-based grammars and the adaptive capabilities of LLMs, we can make significant strides in Bible translation for low-resource languages. It’s a step towards leveraging powerful probabilistic models in a way that does not clobber much rarer and not-yet-digitized languages.

In conclusion, the integration of rule-based grammars with LLMs presents a promising avenue for enhancing Bible translation into low-resource languages. It’s a testament to the power of combining traditional methods with cutting-edge technology – a blend of the old and the new to achieve something truly remarkable in the realm of linguistic translation.

I’ll post some updates when I finally get around to applying the OpenText 2.0 grammar definition to an LLM. I’m excited to see what happens!

Recent Developments in 2024

The landscape of grammar constraints has evolved significantly since this article was first written. Some key developments include:

Inference-Time Reasoning

Rather than just relying on explicit grammar rules, newer models like OpenAI’s o-series demonstrate the ability to perform structured reasoning during inference time. This allows for more naturally constrained outputs without requiring formal grammar definitions.

Hybrid Approaches

Modern constraint systems like LMQL now combine:

Token-level masking with eager validation
High-level text constraints that abstract away tokenization details
Automatic translation between constraint definitions and implementation

This makes it much more practical to implement grammar constraints while maintaining the flexibility of modern LLMs.

Synthetic Data for Structure

Labs are increasingly using synthetic data to bake structural constraints directly into model training. As noted in the Phi-4 technical report, this creates more direct relationships between tokens and helps models learn cleaner reasoning patterns.

Advanced Constraint Techniques

Recent research has introduced more sophisticated approaches to grammar constraints:

Adaptive Sampling: New methods like ASAp (Adaptive Sampling with Approximate Expected Futures) help maintain output quality while enforcing grammar rules
Specialized Frameworks: Tools like YieldLang are making it easier to implement effective grammar constraints
Distribution-Aware Constraints: Researchers are developing methods to maintain the LLM’s natural distribution while enforcing grammatical rules

Grammar Constraints on LLMs

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