Show HN: Concept-Vector–human-interpretable word embeddings

Category: ai-ml

Tags: word-embeddings, interpretability, llm

Score: 3.8/10 (Innovation: 5, Technical: 4, Documentation: 4, Utility: 2)

Concept-Vector proposes replacing traditional word embeddings with deterministic, human-interpretable semantic vectors distilled from LLMs, targeting interpretability and reduced dimensionality. While the idea of explicit semantic components for word embeddings is novel, the project is in an early prototype stage with very limited implementation and no active community. Its current utility is mainly academic and requires substantial further development to be practical.

Target audience: ai researchers, nlp engineers

Repository: https://github.com/truehumanexe/concept_vector/tree/main · Jupyter Notebook

View on Hacker News