Meaning Machine – Visualize how LLMs break down and simulate meaning
10 comments
·April 22, 2025andai
See also: explainer post: https://theperformanceage.com/p/how-language-models-see-you
georgewsinger
Is this really how SOTA LLMs parse our queries? To what extent is this a simplified representation of what they really "see"?
jdspiral
Yes, tokenization and embeddings are exactly how LLMs process input—they break text into tokens and map them to vectors. POS tags and SVOs aren't part of the model pipeline but help visualize structures the models learn implicitly.
sherdil2022
Great job! Do you have any plans to visualize/explain how machine translation - between human languages - works?
jdspiral
Thanks! Yes — that’s on the roadmap, along with some other cool visualizations I’m working on. Machine translation is definitely something I want to work on: showing how models align meaning across languages using shared embeddings and attention patterns. I’d love to make that interactive too.
sherdil2022
I would love to get involved with that (I speak a handful of himan languages). Let me know if you are looking for collaborators.
Dwedit
Send tokens to model, model goes brrrr, get output tokens back.
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[flagged]
I built a tool called Meaning Machine to let you see how language models "read" your words.
It walks through the core stages — tokenization, POS tagging, dependency parsing, embeddings — and visualizes how meaning gets fragmented and simulated along the way.
Built with Streamlit, spaCy, BERT, and Plotly. It’s fast, interactive, and aimed at anyone curious about how LLMs turn your sentence into structured data.
Would love thoughts and feedback from the HN crowd — especially devs, linguists, or anyone working with or thinking about NLP systems.
GitHub: https://github.com/jdspiral/meaning-machine Live Demo: https://meaning-machine.streamlit.app