Project Info
This project did not submit a demo video on Devpost.
Inspiration
Make language models more aligned and capable
What it does
Explores
How we built it
Using HPC, use various models with these methods
Challenges we ran into
The methods are very new, so the accompanying code for the in-focus research was, at times, buggy
Accomplishments we're proud of
Found out how to make models more or less logical. These seem to be important findings. Found a way to estimate what a language model considers to be logical/truthful. Finding interesting tendencies, that don’t appear to be known
What we learned
That I should work on this novel topic more
What's next
for RepE Investigations A paper mapping out truth-conditional semantics in language models. Hypothesis: large language models form a (Tarski-an) metalanguage over their training set, and are probabilistic reasoners over this metalanguage
This project did not link a GitHub repository.
Analysis
No indexed repository for this project, so there are no commit stats to show.
Technology
- PythonUnchecked
- PyTorchUnchecked
No repository was indexed for this project, so these Devpost claims have not been checked against code.
AI coding agents
No repository was indexed, so agent usage could not be checked.
Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.
Codebase size
No repository was indexed, so there is no codebase to measure.
This project did not link a GitHub repository, so there is nothing to diagram.
This project did not link a GitHub repository, so its feature claims have not been checked against code.
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