S-DAT: A Multilingual, GenAI-Driven Framework for Automated Divergent Thinking Assessment

• URL: http://arxiv.org/abs/2505.09068v1
• Date: Wed, 14 May 2025 02:08:40 GMT
• Title: S-DAT: A Multilingual, GenAI-Driven Framework for Automated Divergent Thinking Assessment
• Authors: Jennifer Haase, Paul H. P. Hanel, Sebastian Pokutta,
• Abstract summary: This paper introduces S- DAT (Synthetic-Divergent Association Task), a scalable, multilingual framework for automated assessment of divergent thinking (DT)<n>We evaluate S- DAT across eleven diverse languages, including English, Spanish, German, Russian, Hindi, and Japanese (Kanji, Hiragana, Katakana)<n>Unlike prior DAT approaches, the S- DAT shows convergent validity with other DT measures and correct discriminant validity with convergent thinking.
• Score: 23.509294903995745
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces S-DAT (Synthetic-Divergent Association Task), a scalable, multilingual framework for automated assessment of divergent thinking (DT) -a core component of human creativity. Traditional creativity assessments are often labor-intensive, language-specific, and reliant on subjective human ratings, limiting their scalability and cross-cultural applicability. In contrast, S-DAT leverages large language models and advanced multilingual embeddings to compute semantic distance -- a language-agnostic proxy for DT. We evaluate S-DAT across eleven diverse languages, including English, Spanish, German, Russian, Hindi, and Japanese (Kanji, Hiragana, Katakana), demonstrating robust and consistent scoring across linguistic contexts. Unlike prior DAT approaches, the S-DAT shows convergent validity with other DT measures and correct discriminant validity with convergent thinking. This cross-linguistic flexibility allows for more inclusive, global-scale creativity research, addressing key limitations of earlier approaches. S-DAT provides a powerful tool for fairer, more comprehensive evaluation of cognitive flexibility in diverse populations and can be freely assessed online: https://sdat.iol.zib.de/.

Related papers

• A Comprehensive Evaluation of Multilingual Chain-of-Thought Reasoning: Performance, Consistency, and Faithfulness Across Languages [48.68444770923683]
  We present the first comprehensive study of multilingual Chain-of-Thought (CoT) reasoning.<n>We measure language compliance, answer accuracy, and answer consistency when LRMs are prompt-hacked to think in a target language.<n>We find that the quality and effectiveness of thinking traces vary substantially depending on the prompt language.
  arXiv  Detail & Related papers  (2025-10-10T17:06:50Z)
• Multilingual Self-Taught Faithfulness Evaluators [11.200203292660758]
  Self-Taught Evaluators for Multilingual Faithfulness is a framework that learns exclusively from synthetic multilingual summarization data.<n>Our framework shows improvements over existing baselines, including state-of-the-art English evaluators and machine translation-based approaches.
  arXiv  Detail & Related papers  (2025-07-28T12:01:59Z)
• AdaMCoT: Rethinking Cross-Lingual Factual Reasoning through Adaptive Multilingual Chain-of-Thought [40.16140566668239]
  We introduce AdaMCOT, a framework that enhances multilingual factual reasoning.<n>AdaMCOT dynamically routing thought processes in intermediary "thinking languages" before generating target-language responses.<n>Our evaluation demonstrates substantial improvements in both factual reasoning quality and cross-lingual consistency.
  arXiv  Detail & Related papers  (2025-01-27T15:48:57Z)
• Applications of Artificial Intelligence for Cross-language Intelligibility Assessment of Dysarthric Speech [13.475654818182988]
  This commentary introduces a conceptual framework to advance cross-language intelligibility assessment of dysarthric speech.<n>We propose a universal speech model that encodes dysarthric speech into acoustic-phonetic representations, followed by a language-specific intelligibility assessment model.
  arXiv  Detail & Related papers  (2025-01-27T08:35:19Z)
• Enhancing Multilingual ASR for Unseen Languages via Language Embedding Modeling [50.62091603179394]
  Whisper, one of the most advanced ASR models, handles 99 languages effectively.<n>However, Whisper struggles with unseen languages, those not included in its pre-training.<n>We propose methods that exploit these relationships to enhance ASR performance on unseen languages.
  arXiv  Detail & Related papers  (2024-12-21T04:05:43Z)
• Lens: Rethinking Multilingual Enhancement for Large Language Models [70.85065197789639]
  Lens is a novel approach to enhance multilingual capabilities of large language models (LLMs) It operates by manipulating the hidden representations within the language-agnostic and language-specific subspaces from top layers of LLMs. It achieves superior results with much fewer computational resources compared to existing post-training approaches.
  arXiv  Detail & Related papers  (2024-10-06T08:51:30Z)
• A Systematic Study of Performance Disparities in Multilingual Task-Oriented Dialogue Systems [68.76102493999134]
  We take stock of and empirically analyse task performance disparities that exist between multilingual task-oriented dialogue systems. We prove the existence of the adaptation and intrinsic biases in current ToD systems. Our analyses offer practical tips on how to approach ToD data collection and system development for new languages.
  arXiv  Detail & Related papers  (2023-10-19T16:41:44Z)
• Cross-Lingual Dialogue Dataset Creation via Outline-Based Generation [70.81596088969378]
  Cross-lingual Outline-based Dialogue dataset (termed COD) enables natural language understanding. COD enables dialogue state tracking, and end-to-end dialogue modelling and evaluation in 4 diverse languages.
  arXiv  Detail & Related papers  (2022-01-31T18:11:21Z)
• AM2iCo: Evaluating Word Meaning in Context across Low-ResourceLanguages with Adversarial Examples [51.048234591165155]
  We present AM2iCo, Adversarial and Multilingual Meaning in Context. It aims to faithfully assess the ability of state-of-the-art (SotA) representation models to understand the identity of word meaning in cross-lingual contexts. Results reveal that current SotA pretrained encoders substantially lag behind human performance.
  arXiv  Detail & Related papers  (2021-04-17T20:23:45Z)
• Crossing the Conversational Chasm: A Primer on Multilingual Task-Oriented Dialogue Systems [51.328224222640614]
  Current state-of-the-art ToD models based on large pretrained neural language models are data hungry. Data acquisition for ToD use cases is expensive and tedious.
  arXiv  Detail & Related papers  (2021-04-17T15:19:56Z)
• XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning [68.57658225995966]
  Cross-lingual Choice of Plausible Alternatives (XCOPA) is a typologically diverse multilingual dataset for causal commonsense reasoning in 11 languages. We evaluate a range of state-of-the-art models on this novel dataset, revealing that the performance of current methods falls short compared to translation-based transfer.
  arXiv  Detail & Related papers  (2020-05-01T12:22:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.