Grammar-Aware AI — Product Brief V4

The Problem

Current LLMs treat every word identically — whether it's a subject, a verb, or an adjective. Grammatical roles are entangled in a single embedding space, making models harder to interpret, control, or improve. There is no way to surgically modify one aspect of generated text without re-generating everything.

The Solution

We add seven grammatically orthogonal embedding subspaces — one per grammatical role — plus a three-part dependency system that detects when orthogonality is violated and corrects for it automatically. The result is a more accurate, interpretable, and controllable language model that outperforms models 3× its size and improves retrieval quality.

51.4%

Language model quality improvement

361 → 176 perplexity vs identical-architecture baseline (20 epochs, monotonic improvement)

+1.55

Retrieval NDCG@10 improvement

Grammar fine-tuning vs grammar-free fine-tuning on SciFact — consistent across all 3 epochs

20/21

Grammatical role pairs orthogonal

95.2% — without explicit supervision, confirmed across LM and retrieval domains

Parameter Efficiency

Our 51M grammar model outperforms a 151M grammar-free model by 78% (176 vs 313 PPL) after equal training — using 66% fewer parameters. The 151M unstructured model degraded to 533 PPL by epoch 20. Our model improved monotonically. Grammatical structure acts as a built-in regularizer.

7B Scale

Dependency detection confirmed model-agnostic at Mistral 7B scale. NS↔NO remains the dominant role correlation across three completely different model types — proving the detection methodology is not a small-model artifact.

Retrieval Testing

Grammar embedding fine-tuning improved SciFact NDCG@10 by +1.55 points over identical grammar-free fine-tuning using bge-base-en-v1.5 (architecturally similar to Cohere Embed v3). The NS↔NO dependency was confirmed as dominant in the grammar retrieval bi-encoder — completing a three-model, two-task validation.

NS↔NO Dependency — Three-Model, Two-Task Validation

51M additive LM (from scratch) 0.25840.08296,258

Mistral 7B (pretrained LM) 0.10970.01671,630

Grammar bi-encoder retrieval (fine-tuned) 0.21910.04152,000

🧠

1. Grammatical Embeddings

Seven Role-Specific Subspaces

Subject noun, verb, object noun, modifiers, prepositions. Each token receives a full-dimension base embedding plus a small, learnable role offset. No information loss — every dimension is used.

🔍

2. Dependency Detection

Automated Correlation Analysis

Measures pairwise Pearson correlation between role activations across 2,000–6,258 passages. Identifies NS↔NO as the dominant dependency across LM and retrieval tasks. Model-agnostic and task-agnostic.

🛡

3. Dependency Correction

Linear Correction Modules

Trains lightweight correction modules per detected dependency. Reduces mean role correlation by 21–32% depending on model. Assigns adaptive weights based on dependency scores.

Customer Use Cases

AI Labs (Cohere, Mistral): integrate into training/fine-tuning pipeline, improve quality 51%+ (LM) and retrieval quality on structured queries
Enterprise AI (legal, medical, finance): fine-tune existing LLMs with grammatical structure for domain-specific accuracy
Semantic Search: role-separated embeddings with dependency-corrected similarity — +1.55 NDCG@10 demonstrated on SciFact
Controllable Generation: fix certain grammatical roles while varying others
Interpretability: explain which grammatical elements drove model output

IP Status & Next Steps

Provisional patent filed — priority date established (2006 concept)
Defensive publication protects broad concepts
Non-provisional filing in preparation
All code, checkpoints, and logs preserved for enablement
Proxy retrieval test completed on Cohere-relevant benchmark

Open Now

🚀 Pilot customer conversations
💰 Seed round — $3–5M target
💡 Licensing discussions — AI labs & platforms

Grammar-Aware AIGrammatical Orthogonality for Large Language Models