Breaking Tokens: A New Era with Meta’s Large Concept Models
Sayan Mondal🚀 Introduction
In my journey through the ever-evolving world of AI, I often find myself questioning the building blocks of how machines "understand" language. Most Large Language Models (LLMs) operate at the token level — predicting the next word in a sequence.
But what if we could take a step back, and let models reason like humans do — at the sentence level, or even higher? That’s exactly what Meta’s Large Concept Models (LCMs) aim to do.
In this blog, I’ll walk you through the fascinating paper titled "Large Concept Models: Language Modeling in a Sentence Representation Space" and why I believe it's a big leap toward reasoning-first AI.
🧠 What is a Concept?
Meta defines a "concept" as an abstract unit of meaning — typically a sentence. Instead of generating one word at a time, LCMs generate sentence embeddings, allowing the model to operate at a higher semantic level.
These embeddings are language-agnostic, and powered by SONAR, a multilingual encoder-decoder system supporting over 200 languages.
🏗️ The Architecture: Reasoning in Embedding Space
Here’s how it works:
Input text is split into sentences.
Each sentence is converted into an embedding using SONAR.
The LCM processes these embeddings to predict future sentence embeddings.
The output embeddings are decoded back into text using SONAR.
💡 Key Insight: The model doesn’t care about the language or modality — it just reasons in the concept space.
🔍 Model Variants
Meta introduced three model variants:
Base-LCM – Uses simple MSE loss to predict the next sentence embedding.
Diffusion-LCM – Inspired by image generation (like DALL·E), this model learns a probability distribution over possible sentence embeddings.
Quantized-LCM – Embedding space is discretized using residual vector quantization for more controlled generation.
🧪 Results That Speak
Zero-shot generalization: Works impressively well across languages.
Instruction tuning: Models were trained to follow story-writing prompts — and showed coherence on par with LLaMA-style models.
Diffusion-LCMs outperformed the others in both coherence and relevance.
💭 Why This Matters to Me
Reading this paper was a moment of pause. As I work toward becoming a better AI engineer and aim for roles at product companies like Google, I realize that the future isn’t just about bigger models — it’s about smarter abstractions.
LCMs challenge the norm. They ask us to imagine a world where models don’t mimic words — they understand meaning. That’s a future I want to build.
🔗 Want to Dive Deeper?
✍️ Final Thoughts
As I continue exploring Generative AI and plan my transition into impactful product roles, I’m excited by work that dares to think differently.
Meta’s Large Concept Models remind us: we can go beyond tokens — and closer to human-like reasoning.
If you’re on a similar journey, follow along. Let’s build the future, one concept at a time.
Subscribe to my newsletter
Read articles from Sayan Mondal directly inside your inbox. Subscribe to the newsletter, and don't miss out.
Written by