Summary of Facilitating Large Language Model Russian Adaptation with Learned Embedding Propagation, by Mikhail Tikhomirov and Daniil Chernyshev

Facilitating large language model Russian adaptation with Learned Embedding Propagation

by Mikhail Tikhomirov, Daniil Chernyshev

First submitted to arxiv on: 30 Dec 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary The paper addresses the limitations of open-source large language models (LLMs) by proposing a novel approach called Learned Embedding Propagation (LEP). The authors note that current LLMs, such as GPT-4, have high-quality text generation capabilities but lack transparency in their training data. This exclusivity reduces the benefits of training language-specific LLMs and hinders cost-efficient options like vocabulary extension and pre-training. To overcome these limitations, the paper presents LEP, a method that requires minimal training data size and can implant new language knowledge into existing instruct-tuned variants without instruction-tuning. The authors evaluate four Russian vocabulary adaptations for LLaMa-3-8B and Mistral-7B, showing competitive performance comparable to traditional instruction-tuning methods.
Low	GrooveSquid.com (original content)	Low Difficulty Summary The paper solves a big problem with open-source large language models. These models are very good at generating text, but the people who made them don’t share how they trained them. This makes it hard for others to make their own language-specific models or improve existing ones. The authors of this paper found a way to fix this by creating a new method that can take an existing model and add new language knowledge to it without needing all that training data. They tested this method on some Russian vocabulary adaptations and showed that it works just as well as the old way.