Project summary

• I’m an independent researcher working on improving the reliability and trustworthiness of language models. I have a PhD from Cambridge University and have authored 30 publications in computational science witn an h-index of 21..

• The project involves understanding, detecting and correcting haĺlucinations and output errors in large language models.

• This work has been developed over the past several months as a self-funded effort.

• Supporting final compute experiments for an independent research project on improving reliability in language models.

• I’m requesting a small amount of support to cover GPU compute for these final runs.

• I will update the arXiv link once the paper is completed.

What are this project's goals? How will you achieve them?

• Complete the final experimental validation required for publication.

• This will be achieved by running additional GPU compute experiments to validate results across multiple model architectures and to assess scaling, robustness, and ablation behaviour prior to submission.

How will this funding be used?

Cloud GPU compute for final experimental runs, approximately 200 hours on an NVidia A100.

What are the most likely causes and outcomes if this project fails?

The main risks are operational rather than conceptual. These include GPU availability, runtime issues, or inefficiencies in managing compute jobs.

If delays occur, the outcome would be a slower completion of the final experiments rather than a failure of the project itself. The core work and methodology are already established, so the risk is primarily around execution timing.

How much money have you raised in the last 12 months, and from where?

None so far.