Repository navigation
Connect early n-gram embeddings to learned n-gram memory in LLMs - #39
Merged
Merged
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
The engine positioning now has an explicit historical background: learned n-gram tables connect early word-embedding applications with newer learned n-gram memory modules in LLMs. README, artifact card and roadmap identify FastText and Engram as examples across these application generations, with trained-vector indexing/storage/query as the common systems problem.
Character versus token keys, addressing, training and context fusion remain distinct. Table-based n-gram memory is described as one form of parametric memory; the original FastText paper's experiments retain their scope and later integrations keep their separate software evidence.
Validation:
git diff --checkpasses; all 40 local link targets in the three edited guides exist. Documentation only; no runtime or benchmark changes. Existing CI verifies compatibility and integrations.