Why Standard LLM Deduplication Erases Rare Knowledge: Meet MI Guard

The Hidden Trap in Data Pruning

When preparing domain datasets for fine-tuning, standard industry advice is simple: *"Deduplicate aggressively using MinHash or cosine similarity."*

While this works for web-scraped crawl data, it is disastrous for enterprise knowledge bases. In specialized domains—such as legal compliance, clinical diagnostics, or internal DevOps runbooks—high-frequency facts are generic, while the most critical business rules appear only once or twice.

When standard deduplication runs, the density of generic samples overwhelms the distribution, and rare but vital edge cases are discarded as noise.

The Mathematics of MI Guard

MoroAI solves this through Epistemic Mutual Information (MI) Guarding.

For every candidate sample $x_i$, we compute its conditional information gain relative to the core semantic distribution $P(X)$:

$$I(x_i; X) = H(X) - H(X | x_i)$$

Where $H(X)$ represents the Shannon entropy of the token transition matrix. If $I(x_i; X)$ exceeds the configured safeguard threshold $\tau_{\text{guard}}$, the sample is classified as an Epistemic Boundary Landmark.

The Result: 0 Rare Facts Lost

In our benchmark on 40,000 corporate operational logs: - Standard MinHash pruned 41% of rare compliance exceptions. - MoroAI with MI Guard pruned 2,890 redundant greetings and boilerplate queries while preserving 100% of the tagged compliance boundary cases.

You no longer have to choose between clean datasets and comprehensive domain coverage.