# Generative Model for Less-Resourced Language with 1 billion parameters Source: [Generative Model for Less-Resourced Language with 1 billion parameters]() Collection index: [llms.txt]() Data updated: 2025-10-26T03:27:16.108Z Authors: Domen Vre\\v{s}, Martin Bo\\v{z}i\\v{c}, Alja\\v{z} Poto\\v{c}nik, Toma\\v{z} Martin\\v{c}i\\v{c}, Marko Robnik-\\v{S}ikonja Published: 2024-10-09T13:59:34.000Z ## Abstract Large language models (LLMs) are a basic infrastructure for modern natural language processing. Many commercial and open-source LLMs exist for English, e.g., ChatGPT, Llama, Falcon, and Mistral. As these models are trained on mostly English texts, their fluency and knowledge of low-resource languages and societies are superficial. We present the development of large generative language models for a less-resourced language. GaMS 1B - Generative Model for Slovene with 1 billion parameters was created by continuing pretraining of the existing English OPT model. We developed a new tokenizer adapted to Slovene, Croatian, and English languages and used embedding initialization methods FOCUS and WECHSEL to transfer the embeddings from the English OPT model. We evaluate our models on several classification datasets from the Slovene suite of benchmarks and generative sentence simplification task SENTA. We only used a few-shot in-context learning of our models, which are not yet instruction-tuned. For classification tasks, in this mode, the generative models lag behind the existing Slovene BERT-type models fine-tuned for specific tasks. On a sentence simplification task, the GaMS models achieve comparable or better performance than the GPT-3.5-Turbo model. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [ESG Sentiment Analysis on SloESG-News Aspect E 1.0 (test)](): ESG Sentiment Analysis; SloESG-News Aspect E 1.0 (test); Accuracy: 80. - [Sentiment Analysis on SloESG-News 1.0 (test)](): Sentiment Analysis; SloESG-News 1.0 (test); Accuracy: 60. ## Original sources and implementation [Original paper]() [PDF]()