Summary of Vikhr: Constructing a State-of-the-art Bilingual Open-source Instruction-following Large Language Model For Russian, by Aleksandr Nikolich et al.
Vikhr: Constructing a State-of-the-art Bilingual Open-Source Instruction-Following Large Language Model for Russian
by Aleksandr Nikolich, Konstantin Korolev, Sergei Bratchikov, Igor Kiselev, Artem Shelmanov
First submitted to arxiv on: 22 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 A surge in Large Language Models (LLMs) has led to significant challenges when adapting these models to languages other than English. These issues include poor generation quality and reduced computational performance due to disproportionate token representation. To address this, researchers developed a pipeline for adapting pre-trained English-oriented LLMs to other languages and constructing efficient bilingual LLMs. The resulting model, Vikhr, is a state-of-the-art open-source instruction-following LLM designed specifically for the Russian language. Unlike previous models, Vikhr features an adapted tokenizer vocabulary and undergoes continued pre-training and instruction tuning of all weights. This approach enhances performance while improving computational and contextual efficiency. Vikhr’s remarkable performance across various Russian-language benchmarks can be attributed to expanded instruction datasets and corpora for continued pre-training. Vikhr not only sets a new state-of-the-art among open-source LLMs for Russian but also outperforms some proprietary closed-source models on certain benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to make a machine learn a new language, like Russian. It’s hard because the machine was trained mainly on English words and doesn’t understand Russian well. To solve this problem, scientists created a special way to adapt machines trained on English to other languages like Russian. They made a model called Vikhr that can understand and follow instructions in Russian. This is different from previous models that were less good at understanding Russian because they didn’t have the right words and didn’t train as much. Vikhr is very good at following instructions in Russian and even beats some other, secret models on certain tests. |
Keywords
» Artificial intelligence » Instruction tuning » Token » Tokenizer
