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SnakModel: Lessons Learned from Training an Open Danish Large Language Model
No full textWe present SnakModel, a Danish large language model (LLM) based on Llama2-7B, which we continuously pre-train on 13.6B Danish words, and further tune on 3.7M Danish instructions. As best practices for creating LLMs for smaller language communities have yet to be established, we examine the effects of early modeling and training decisions on downstream performance throughout the entire training pipeline, including (1) the creation of a strictly curated corpus of Danish text from diverse sources; (2) the language modeling and instruction-tuning training process itself, including the analysis of intermediate training dynamics, and ablations across different hyperparameters; (3) an evaluation on eight language and culturally-specific tasks. Across these experiments SnakModel achieves the highest overall performance, outperforming multiple contemporary Llama2-7B-based models. By making SnakModel, the majority of our pre-training corpus, and the associated code available under open licenses, we hope to foster further research and development in Danish Natural Language Processing, and establish training guidelines for languages with similar resource constraints
Uncontrolled eating - the only factor in eating psychology? Psychometric analysis of eating behavior items and development of a short questionnaire
No full textTänapäeva ühiskonnas on kasvavaks probleemiks kujunenud ülekaalulisus ja rasvumine. Nende nähtusete psühholoogiliste tagamaade uurimiseks on loodud suurel hulgal küsimustikke, mis aga kipuvad olema võrdlemisi sarnased. Magistritöö eesmärgiks oli luua seitsme levinuma söömisküsimustiku põhjal lühiküsimustik ja vaadelda, kui suures osas on eri söömisküsimustike variatiivsus seletatav jagatud kontrollimatu söömise latentse faktoriga. Töö lähtub mittehomöostaatilise söömise ja kontrollimatu söömise teooriatest. Varem kogutud andmetel viidi läbi korreleeritud faktoritega faktoranalüüs, bifaktoranalüüs ja regressioonanalüüs väliste tunnustega – kehamassiindeks, depressiooni ja ärevuse sümptomid, alkoholi problemaatiline tarbimine, emotsiooniregulatsiooni raskused ja kliiniline häiritus. Analüüsi käigus leiti, et a) küsimustike lühendamine faktormudeli alusel võimaldab säilitada originaalküsimustike faktorstruktuuri; b) suur osa kasutatud küsimustike variatiivsusest on seletav kontrollimatu söömisega (bifaktormudelis on kontrollimatul söömisel keskmine korrelatsioon väidetega 0,55, spetsiifiliste alafaktoritega mudelis jääb keskmine korrelatsioon aga 0,41 tasemele); c) kontrollimatu söömine näitas regressioonianalüüsis suuremat statistilist ennustusvõimet välistele tunnustele kui alafaktorid. Ilmnenud kontrollimatu söömise suur roll ja tugev seos emotsiooniregulatsiooni raskustega viitavad vajadusele uurida ja laiendada transdiagnostilisi sekkumisi problemaatilise söömiskäitumise ohjamiseks
No AI on a Dead Planet: Sentiment and Emotion Analysis Across Reddit Communities on AI and the Environment
No full textThis paper investigates how different online communities perceive and discuss the environmental impact of AI through sentiment analysis and emotion detection. We analyze Reddit discussion from r/artificial and r/climatechange, using pre-trained models fine-tuned on social media data. Our analysis reveals distinct patterns in how these communities engage with AI's environmental implications: the AI community demonstrates a shift from predominantly neutral and positive sentiment in posts to more balanced perspectives in comments, while the climate community maintains a more critical stance throughout discussions. The findings contribute to our understanding of how different communities conceptualize and respond to the environmental challenges of AI development
Encoder vs Decoder: Comparative Analysis of Encoder and Decoder Language Models on Multilingual NLU Tasks
Get PDFThis paper explores the performance of encoder and decoder language models on multilingual Natural Language Understanding (NLU) tasks, with a broad focus on Germanic languages. Building upon the ScandEval benchmark, initially restricted to evaluating encoder models, we extend the evaluation framework to include decoder models. We introduce a method for evaluating decoder models on NLU tasks and apply it to the languages Danish, Swedish, Norwegian, Icelandic, Faroese, German, Dutch, and English. Through a series of experiments and analyses, we also address research questions regarding the comparative performance of encoder and decoder models, the impact of NLU task types, and the variation across language resources. Our findings reveal that encoder models can achieve significantly better NLU performance than decoder models despite having orders of magnitude fewer parameters. Additionally, we investigate the correlation between decoders and task performance via a UMAP analysis, shedding light on the unique capabilities of decoder and encoder models. This study contributes to a deeper understanding of language model paradigms in NLU tasks and provides valuable insights for model selection and evaluation in multilingual settings
The effect of personality on the variability of adverse reaction reporting for drugs and vaccines
Get PDFKäesoleva uurimistöö eesmärgiks oli uurida isiksuse komponentide mõju objektiivsete kõrvaltoimete tekkimisel tavaravimite ja vaktsiinide seas. Andmete kogumise protsessi koostas ja viis läbi Eesti Geenivaramu, mille käigus vastas isiksuseküsimustikule (NP-100) ja Ravimi kõrvaltoimete küsimustikule ligikaudu 22 000 inimest. Uuringu tulemusena saab Viie-faktorilise isiksusemudeliga seostada kõrvaltoimete raporteerimisega neurootilisust ning avatust kogemustele. Neurootilisuse tase on seoses nii abnormaalsete (villid, maksakahjustus, anafülaktiline šokk) kui suure hulga (4 ja rohkem) kõrvaltoimete tekkega. Tavaravimite ja vaktsiinide kõrvaltoimete omavahelisel võrdlusel on vaktsiinide kõrvaltoimete raporteerijate neurootilisuse tase kõrgem
Braxen 1.0
No full textWith this paper, we release a Swedish pronunciation lexicon resource, Braxen 1.0, which is the result of almost 20 years development carried out at the Swedish Agency for Accessible Media (MTM). The lexicon originated with a basic word list, but has continuously been exanded with new entries, mainly acquired from university textbooks and news text. Braxen consists of around 850 000 entries, of which around 150 000 are proper names. The lexicon is released under the CC BY 4.0 license and is accessible for public use
The association between autism spectrum traits and symptoms of eating disorders and avoidant/restrictive food intake disorder
Get PDFKäesoleva töö eesmärgiks oli uurida autismispektri häire (ASH) joonte seoseid söömishäirete ja vältiva/piirava toitumishäire (ARFID) sümptomitega. Uuringus osales 150 inimest vanusevahemikus 16-65, kes täitsid enesekohased küsimustikud vältiva/piirava toitumishäire, söömishäirete, ASH, ärevuse ja depressiooni kohta. Seoste hindamiseks viidi läbi korrelatsioon- ja regressioonanalüüsid. Tulemustest selgus, et ASH-le omased sotsiaalsete oskuste ja tähelepanu ümberlülitamise puudulikkus olid seoses söömise piiramisega. Veel ilmnes seos ASH joonte kommunikatsiooni puudulikkuse ja detailitähelepanu ning ARFID-i sensoorsest tundlikkusest tuleneva toidu vältimise profiili vahel. Lisaks leiti, et ASH joonte, ärevuse ja depressiooni sümptomite koosmõju ennustas nii söömishäirete kui ARFID-i sümptomite esinemist. ASH kui ka söömis- ja toitumishäirete esinemissageduse kasv suurendab vajadust tuvastada nende häirete vahelisi võimalikke seoseid, et jõuda paremate ravivõimaluste ning ennetusmeetmeteni
Prompt Engineering Enhances Faroese MT, but Only Humans Can Tell
No full textThis study evaluates GPT-4's English-to-Faroese translation capabilities, comparing it with multilingual models on FLORES-200 and Sprotin datasets. We propose a prompt optimization strategy using Semantic Textual Similarity (STS) to improve translation quality. Human evaluation confirms the effectiveness of STS-based few-shot example selection, though automated metrics fail to capture these improvements. Our findings advance LLM applications for low-resource language translation while highlighting the need for better evaluation methods in this context
Hotter and Colder: A New Approach to Annotating Sentiment, Emotions, and Bias in Icelandic Blog Comments
No full textThis paper presents Hotter and Colder, a dataset designed to analyze various types of online behavior in Icelandic blog comments. Building on previous work, we used GPT-4o mini to annotate approximately 800,000 comments for 25 tasks, including sentiment analysis, emotion detection, hate speech, and group generalizations. Each comment was automatically labeled on a 5-point Likert scale. In a second annotation stage, comments with high or low probabilities of containing each examined behavior were subjected to manual revision. By leveraging crowdworkers to refine these automatically labeled comments, we ensure the quality and accuracy of our dataset resulting in 12,232 uniquely annotated comments and 19,301 annotations. Hotter and Colder provides an essential resource for advancing research in content moderation and automatically detecting harmful online behaviors in Icelandic. We release both the dataset and annotation interface