| 1. |
- Ekgren, A., et al.
(författare)
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Lessons Learned from GPT-SW3 : Building the First Large-Scale Generative Language Model for Swedish
- 2022
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Ingår i: 2022 Language Resources and Evaluation Conference, LREC 2022. - : European Language Resources Association (ELRA). - 9791095546726 ; , s. 3509-3518
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Konferensbidrag (refereegranskat)abstract
- We present GPT-SW3, a 3.5 billion parameter autoregressive language model, trained on a newly created 100 GB Swedish corpus. This paper provides insights with regard to data collection and training process, and discusses the challenges of proper evaluation. The results of quantitive evaluation using perplexity indicate that GPT-SW3 is a competent model in comparison with existing autoregressive models of similar size. Additionally, we perform an extensive prompting study which reveals the good text generation capabilities of GPT-SW3. © licensed under CC-BY-NC-4.0.
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| 2. |
- Dahlberg, Stefan, et al.
(författare)
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A Distributional Semantic Online Lexicon for Linguistic Explorations of Societies
- 2023
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Ingår i: Social Science Computer Review. - : SAGE Publications. - 0894-4393 .- 1552-8286. ; 41:2
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Tidskriftsartikel (refereegranskat)abstract
- Linguistic Explorations of Societies (LES) is an interdisciplinary research project with scholars from the fields of political science, computer science, and computational linguistics. The overarching ambition of LES has been to contribute to the survey-based comparative scholarship by compiling and analyzing online text data within and between languages and countries. To this end, the project has developed an online semantic lexicon, which allows researchers to explore meanings and usages of words in online media across a substantial number of geo-coded languages. The lexicon covers data from approximately 140 language-country combinations and is, to our knowledge, the most extensive free research resource of its kind. Such a resource makes it possible to critically examine survey translations and identify discrepancies in order to modify and improve existing survey methodology, and its unique features further enable Internet researchers to study public debate online from a comparative perspective. In this article, we discuss the social scientific rationale for using online text data as a complement to survey data, and present the natural language processing-based methodology behind the lexicon including its underpinning theory and practical modeling. Finally, we engage in a critical reflection about the challenges of using online text data to gauge public opinion and political behavior across the world.
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