Poro 34B: A Multilingual Language Model Advancing State-of-the-Art for Finnish and Excelling in Translation and Code Generation

Core Concepts

A 34 billion parameter multilingual language model, Poro 34B, trained on 1 trillion tokens of Finnish, English, and programming languages, substantially advances the state-of-the-art for Finnish while also performing competitively in English and code generation, and achieving strong translation capabilities.

Abstract

The paper introduces Poro 34B, a 34 billion parameter multilingual language model trained on 1 trillion tokens of data in Finnish, English, and programming languages. The key insights and findings are: Multilingual training can lift the limitations of data availability for smaller languages like Finnish, allowing the creation of models that substantially outperform previous monolingual Finnish models. Poro 34B not only advances the state-of-the-art for Finnish, but also performs competitively in its class for English and code generation tasks. The model achieves remarkably strong translation capabilities, outperforming dedicated translation models on English-Finnish translation benchmarks. The authors note that while multilingual training can be beneficial, careful choices are required, such as limiting the number of languages, matching scripts, and incorporating cross-lingual signals. The authors release the model, scripts, and data openly, aiming to serve as a template for creating large models for other smaller languages.

Stats

The pretraining data consists of 542B tokens of English, 32B tokens of Finnish, 208B tokens of programming languages, and 8B tokens of English-Finnish translation pairs. The Finnish data is sourced from web crawls, news sources, a copyright-free book corpus, Wikipedia, and online discussion forums. The English data is from the SlimPajama and Dolma corpora, and the programming language data is from the Starcoder corpus.

Quotes

"Multilingual training offers one obvious solution for increasing the amount of training data available, and a large number of multilingual transformer models have been introduced (e.g. Conneau et al., 2020; Lin et al., 2022b; Le Scao et al., 2022; Wei et al., 2023)." "We find that Poro 34B is the best-performing model for Finnish in this comparison, substantially outperforming the best previously introduced monolingual Finnish model." "Poro 34B is a remarkably strong translator, outperforming not only dedicated open-source translation models but even Google Translate, and scoring roughly on par with GPT-4 in this evaluation."

Key Insights Distilled From

Poro 34B and the Blessing of Multilinguality

by Rist... at arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.01856.pdf

Deeper Inquiries

How can the lessons learned from training Poro 34B be applied to create large language models for other smaller languages with limited data availability?

The lessons learned from training Poro 34B can be applied to create large language models for other smaller languages with limited data availability by following a similar multilingual training approach. Some key strategies that can be implemented include: Limited Multilinguality: Instead of including a large number of languages, focusing on a limited set of languages that are closely related or share similar characteristics can be more effective. Matching Scripts and Language Families: Training models on languages with similar scripts or belonging to the same language family can improve performance. This approach helps leverage similarities in linguistic structures and patterns. Incorporating Cross-Lingual Signals: Including translation pairs in the pretraining data can provide a cross-lingual signal that enhances the model's ability to transfer knowledge between languages. Oversampling Target Language Data: To address data limitations for smaller languages, oversampling the target language data during pretraining can help improve the model's proficiency in that language. Augmenting with Diverse Data: Beyond natural language and programming languages, incorporating diverse datasets such as cultural texts, historical documents, or domain-specific information can enrich the model's understanding and improve its performance on a wide range of tasks. By applying these strategies and adapting them to the specific characteristics of each smaller language, it is possible to create large language models that excel in tasks related to those languages despite limited data availability.

What are the potential drawbacks or risks of making powerful generative models like Poro 34B widely available, and how can these be mitigated?

Making powerful generative models like Poro 34B widely available comes with potential drawbacks and risks that need to be addressed to ensure responsible use. Some of these risks include: Bias and Misinformation: Generative models can amplify biases present in the training data and generate misleading or harmful content. Mitigation strategies include bias detection tools, diverse training data, and ethical guidelines for model usage. Privacy Concerns: Models like Poro 34B can inadvertently memorize sensitive information from the training data, posing privacy risks. Implementing data anonymization techniques and limiting access to sensitive data can help mitigate these concerns. Malicious Use: Powerful language models can be exploited for malicious purposes such as generating fake news or engaging in online harassment. Implementing content moderation tools, user verification processes, and ethical guidelines for model usage can help prevent misuse. Environmental Impact: Training large models like Poro 34B requires significant computational resources, leading to a high carbon footprint. Using renewable energy sources for training and optimizing model architecture for efficiency can help reduce environmental impact. By implementing robust governance frameworks, promoting ethical AI practices, and fostering transparency in model development and deployment, the risks associated with widely available generative models can be mitigated effectively.

What other types of data, beyond natural language and programming languages, could be incorporated into the pretraining of large multilingual models to further enhance their capabilities?

Incorporating diverse types of data beyond natural language and programming languages can enhance the capabilities of large multilingual models in various ways. Some additional data sources that can be beneficial for pretraining include: Multimodal Data: Combining text with images, audio, or video data can enable models to understand and generate content across different modalities, enhancing their ability to process and generate rich, multimodal content. Domain-Specific Data: Including domain-specific datasets such as medical records, legal documents, scientific literature, or financial reports can improve the model's performance in specialized tasks and industries. Cultural and Historical Data: Incorporating cultural texts, folklore, historical documents, and artifacts can enrich the model's understanding of diverse cultural contexts and historical events, enabling it to generate culturally relevant content. Geospatial Data: Integrating geospatial information, maps, and location-based data can enhance the model's spatial reasoning and enable it to generate location-specific content or recommendations. User Interaction Data: Utilizing user interaction data, such as social media posts, reviews, or user-generated content, can help the model understand user preferences, sentiments, and behaviors, leading to more personalized and context-aware responses. By incorporating a wide range of diverse data sources into the pretraining process, large multilingual models can develop a more comprehensive understanding of the world and perform effectively across a broader spectrum of tasks and applications.