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Yayın Psychometric properties of the Turkish version of the eating pathology symptoms inventory (EPSI-T)(Cogent OA, 2025) Türk, Fidan; Acet, Pınar; Karabulut, Goncagül; Akay, NazlıThe purpose of this study was to examine the factor structure and psychometric properties of the Turkish version of the Eating Pathology Symptoms Inventory (EPSI‑T), and to explore gender differences in eating disorder symptoms. Participants were 473 university students in Türkiye (342 women, 113 men) who completed the EPSI‑T, along with the Modified Weight Bias Internalization Scale (WBIS‑M), Addiction‑like Eating Behaviour Scale (AEBS), Muscularity‑Oriented Eating Test (MOET), and Depression Anxiety and Stress Scales (DASS‑21). Confirmatory factor analysis supported the original eight‑factor, 45‑item structure [χ2(914) = 1994.57, χ2/df = 2.18, CFI = 0.90, RMSEA = 0.05 (0.05–0.06), SRMR = 0.07]. Women scored significantly higher on most subscales, except for Excessive Exercise, Muscle Building, and Negative Attitudes toward Obesity, where men scored higher (p < 0.005). Reliability was strong, with Cronbach’s α ranging from 0.72 to 0.90 and McDonald’s ω from 0.75 to 0.90. Convergent and discriminant validity were also supported. Overall, findings suggest that the EPSI‑T is a reliable and valid measure of eating disorder symptoms in Turkish‑speaking populations and may facilitate cross‑cultural research by providing a tool structurally consistent with the original English version.Yayın TURSpider: a Turkish Text-to-SQL dataset and LLM-based study(Institute of Electrical and Electronics Engineers Inc., 2024-11-25) Kanburoğlu, Ali Buğra; Tek, Faik BorayThis paper introduces TURSpider, a novel Turkish Text-to-SQL dataset developed through human translation of the widely used Spider dataset, aimed at addressing the current lack of complex, cross-domain SQL datasets for the Turkish language. TURSpider incorporates a wide range of query difficulties, including nested queries, to create a comprehensive benchmark for Turkish Text-to-SQL tasks. The dataset enables cross-language comparison and significantly enhances the training and evaluation of large language models (LLMs) in generating SQL queries from Turkish natural language inputs. We fine-tuned several Turkish-supported LLMs on TURSpider and evaluated their performance in comparison to state-of-the-art models like GPT-3.5 Turbo and GPT-4. Our results show that fine-tuned Turkish LLMs demonstrate competitive performance, with one model even surpassing GPT-based models on execution accuracy. We also apply the Chain-of-Feedback (CoF) methodology to further improve model performance, demonstrating its effectiveness across multiple LLMs. This work provides a valuable resource for Turkish NLP and addresses specific challenges in developing accurate Text-to-SQL models for low-resource languages.
