Stories with Tag multilingual

• Teuken-7B-Base and Teuken-7B-Instruct: Towards European LLMs

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  Posted: 2025-04-15 10:17:17

  Verbose tl;dr

  Researchers introduce Teukten-7B, a new family of 7-billion parameter language models specifically trained on a diverse European dataset. The models, Teukten-7B-Base and Teukten-7B-Instruct, aim to address the underrepresentation of European languages and cultures in existing LLMs. Teukten-7B-Base is a general-purpose model, while Teukten-7B-Instruct is fine-tuned for instruction following. The models are pre-trained on a multilingual dataset heavily weighted towards European languages and demonstrate competitive performance compared to existing models of similar size, especially on European-centric benchmarks and tasks. The researchers emphasize the importance of developing LLMs rooted in diverse cultural contexts and release Teukten-7B under a permissive license to foster further research and development within the European AI community.

  The preprint "Teuken-7B-Base and Teuken-7B-Instruct: Towards European LLMs" introduces two new open-source large language models (LLMs) named Teuk-7B-Base and Teuk-7B-Instruct, developed with a focus on European languages and data privacy. The authors argue for the importance of developing LLMs within Europe to address specific regional needs, maintain data sovereignty, and foster a robust European AI ecosystem. They highlight the risks associated with relying solely on LLMs trained outside the region, particularly concerning data privacy and potential biases reflecting values and cultural norms different from European ones.

  Teuken-7B-Base serves as the foundational model, pre-trained on a diverse multilingual dataset curated with an emphasis on European languages. This dataset, known as "EuroMix-4B," is comprised of text and code drawn from various sources, including Common Crawl, Europarl, and publicly accessible code repositories. The authors detail the data processing pipeline, including filtering for quality, deduplication, and language identification. They also emphasize their focus on data privacy by exclusively using publicly available data and minimizing the inclusion of personally identifiable information (PII).

  Built upon Teuken-7B-Base, Teuken-7B-Instruct is further refined through supervised fine-tuning (SFT) to better align with user instructions and generate more relevant and helpful responses. This fine-tuning process leverages a dataset derived from publicly available instruction datasets translated and augmented for improved performance across European languages. The authors explain the specific techniques used for instruction tuning, including data formatting and optimization strategies.

  The paper presents a comprehensive evaluation of both Teuken-7B-Base and Teuken-7B-Instruct, benchmarking their performance against other existing LLMs across a variety of tasks. These evaluations include standard language modeling benchmarks, as well as specific tests designed to assess their understanding of European languages and cultural contexts. The results demonstrate competitive performance across several metrics, suggesting the efficacy of the proposed training methodology and the value of specializing LLMs for specific regional needs.

  Furthermore, the authors emphasize the open-source nature of both models and the associated training data, aiming to promote transparency and facilitate further research and development within the European AI community. They also highlight the potential applications of these models in various domains, ranging from content generation and translation to code completion and customer service. Finally, the paper concludes by outlining future research directions, including scaling up the model size, expanding the training data to encompass more languages and cultural contexts, and exploring further advancements in fine-tuning techniques to further improve the models' capabilities and their alignment with user expectations.

  • Large Language Models
  • LLMs
  • natural language processing
  • NLP
  • machine learning
  • AI
  • artificial intelligence
  • deep learning
  • European
  • Teuken
  • Teuken-7B-Base
  • Teuken-7B-Instruct
  • Language Model Training
  • arXiv
  • Open Source
  • multilingual

  Summary of Comments ( 72 )
  https://news.ycombinator.com/item?id=43690955

  Verbose tl;dr

  Hacker News users discussed the potential impact of the Teukens models, particularly their smaller size and focus on European languages, making them more accessible for researchers and individuals with limited resources. Several commenters expressed skepticism about the claimed performance, especially given the lack of public access and limited evaluation details. Others questioned the novelty, pointing out existing multilingual models and suggesting the main contribution might be the data collection process. The discussion also touched on the importance of open-sourcing models and the challenges of evaluating LLMs, particularly in non-English languages. Some users anticipated further analysis and comparisons once the models are publicly available.

  The Hacker News post titled "Teuken-7B-Base and Teuken-7B-Instruct: Towards European LLMs" (https://news.ycombinator.com/item?id=43690955) has a modest number of comments, sparking a discussion around several key themes related to the development and implications of European-based large language models (LLMs).

  Several commenters focused on the geopolitical implications of the project. One commenter expressed skepticism about the motivation behind creating "European" LLMs, questioning whether it stemmed from a genuine desire for technological sovereignty or simply a reaction to American dominance in the field. This spurred a discussion about the potential benefits of having diverse sources of LLM development, with some arguing that it could foster competition and innovation, while others expressed concern about fragmentation and duplication of effort. The idea of data sovereignty and the potential for different cultural biases in LLMs trained on European data were also touched upon.

  Another thread of discussion revolved around the technical aspects of the Teuken models. Commenters inquired about the specific hardware and training data used, expressing interest in comparing the performance of these models to existing LLMs. The licensing and accessibility of the models were also raised as points of interest. Some users expressed a desire for more transparency regarding the model's inner workings and training process.

  Finally, a few comments touched upon the broader societal implications of LLMs. One commenter questioned the usefulness of yet another LLM, suggesting that the focus should be on developing better applications and tools that utilize existing models, rather than simply creating more models. Another commenter raised the issue of potential misuse of LLMs and the importance of responsible development and deployment.

  While there wasn't a single overwhelmingly compelling comment, the discussion as a whole provides a valuable snapshot of the various perspectives surrounding the development of European LLMs, touching upon technical, geopolitical, and societal considerations. The comments highlight the complex interplay of factors that influence the trajectory of LLM development and the importance of open discussion and critical evaluation of these powerful technologies.

• European word translator: an interactive map showing words in over 30 languages

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  Posted: 2025-02-23 19:53:03

  Verbose tl;dr

  UK Data Explorer created an interactive map showcasing common words across Europe in over 30 languages. Users can select a word from a list (e.g., "bread," "beer," "house") and see its translation displayed on the map, color-coded by linguistic similarity. The map highlights the diversity and evolution of languages across the continent, revealing interesting etymological relationships and regional variations. It serves as a visual tool for exploring language families and how words have spread and changed over time.

  The UK Data Explorer website hosts an intriguing interactive project titled "European Word Translator," providing a fascinating glimpse into the linguistic diversity of the European continent. This interactive map showcases the nuanced variations in how common words are translated across more than 30 European languages, offering a rich exploration of both linguistic evolution and cultural influences. The user interface presents a map of Europe, allowing users to select a specific word from a predefined list of everyday terms. Upon selection, the map dynamically updates, displaying the corresponding translation of the chosen word in each language represented on the map. Each country is color-coded, with different hues indicating different root words or linguistic origins for the translation. This visual representation allows users to readily identify etymological relationships and linguistic patterns across different geographic regions. Furthermore, hovering the mouse cursor over a specific country reveals not only the translated word but also the language spoken in that country, adding another layer of informational richness to the interactive experience. This tool goes beyond simple translation, serving as a valuable resource for those interested in comparative linguistics, etymology, and the cultural nuances embedded within language. The project highlights the complex tapestry of languages spoken across Europe, illustrating the interconnectedness and divergence of linguistic traditions within a relatively small geographic area. It provides a visually compelling and intellectually stimulating way to explore the rich linguistic heritage of Europe, inviting users to delve into the subtle and sometimes dramatic differences in how we express fundamental concepts across diverse cultural and linguistic landscapes.

  • European languages
  • translator
  • Interactive Map
  • language learning
  • linguistics
  • translation
  • Data Visualization
  • Europe
  • word origins
  • etymology
  • language comparison
  • multilingual
  • lexicography

  Summary of Comments ( 7 )
  https://news.ycombinator.com/item?id=43152587

  Verbose tl;dr

  Hacker News users discussed the methodology and potential issues of the European word translator map. Several commenters pointed out inaccuracies and oversimplifications in the data, particularly regarding dialects and false cognates. Some suggested improvements, like including IPA transcriptions to show pronunciation differences and adding more granular detail to regional variations. The map's visualization choices, such as using size to represent speaker numbers, also drew criticism for being potentially misleading. Others praised the project's overall concept and educational value, acknowledging its limitations while still finding it an interesting tool. There was also discussion about the difficulties of representing linguistic data visually and the complexities of European language families.

  The Hacker News post titled "European word translator: an interactive map showing words in over 30 languages" has generated several comments discussing the linked interactive map of European word translations.

  Several users praised the map's visual presentation and ease of use. One commenter appreciated the ability to see at a glance the geographical distribution of different words for a single concept, highlighting how the map effectively illustrates the spread and evolution of languages across Europe. Another user found the tool useful for understanding the etymology of words, particularly those shared across multiple languages. The clean interface and intuitive design were also commended.

  Some commenters delved into the linguistic aspects of the map, discussing the complexities of translation and regional variations. One noted the interesting discrepancies between official languages and regional dialects, pointing out how the map can reveal nuances not always captured in formal linguistic studies. Another highlighted the importance of context in translation, cautioning against taking the provided translations as absolute equivalents, as subtle meanings can vary depending on usage. A discussion also arose around the challenges of accurately representing linguistic diversity on a map, given the fluidity of language boundaries and the existence of multiple dialects within a single region.

  A few commenters pointed out some specific examples showcased by the map, such as the distribution of different words for "bear," illustrating the linguistic influences of various historical and cultural factors. One user observed how the map could be used as an educational tool, providing a visually engaging way to learn about European languages and their interrelationships.

  Several comments also focused on technical aspects of the map. One commenter inquired about the data sources used to compile the translations, while another suggested potential improvements to the user interface, such as the addition of audio pronunciations. There was also a discussion about the map's limitations, acknowledging that it couldn't capture the full complexity of European linguistic diversity.

  In summary, the comments on the Hacker News post generally express positive sentiment towards the interactive map, praising its visual presentation and educational value. The discussion also touched upon the complexities of translation, the importance of data accuracy, and potential improvements to the tool's functionality.