Nov 25, 2024
Math Speaks All Languages: Enhancing LLM Problem-Solving Across Multilingual Contexts
Maksim Kostritsya, Kseniia Kuvshinova, Rauf Parchiev, Konstantin Polev
Summary
Large language models (LLMs) have shown significant adaptability in tackling various human issues; however, their efficacy in resolving mathematical problems remains inadequate. Recent research has identified steering vectors — hidden attributes that can guide the actions and outputs of LLMs. Nonetheless, the exploration of universal vectors that can consistently affect model responses across different languages is still limited. This project aims to confront two primary challenges in contemporary LLM research by utilizing the Goodfire API to examine whether common latent features can improve mathematical problem-solving capabilities, regardless of the language employed.
Cite this work:
@misc {
title={
Math Speaks All Languages: Enhancing LLM Problem-Solving Across Multilingual Contexts
},
author={
Maksim Kostritsya, Kseniia Kuvshinova, Rauf Parchiev, Konstantin Polev
},
date={
11/25/24
},
organization={Apart Research},
note={Research submission to the research sprint hosted by Apart.},
howpublished={https://apartresearch.com}
}