# Project export: RepE Investigations - Making LLMs logical (plug and play)

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## Project metadata

- Hackathon: Cal Hacks 10.0
- Tagline: We are in the future - we can now ‘read’ and ‘control’ the minds of language models. I take a preliminary look into the novel methods that allow us to do this
- Devpost: https://devpost.com/software/repe-investigations
- GitHub: not linked
- Team: contributor stats unavailable

## Devpost submission (written by the team)

### 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

## README (from the GitHub repository)

No README available.

## Detected evidence (automated analysis)

No repository was indexed for this project. Claimed technologies below could not be checked against code.
- Python (language) — claimed on Devpost, not found in the code
- PyTorch (technology) — claimed on Devpost, not found in the code

## Codebase structure

No repository index available.

## Key source files

No repository index available; no source files included.