Software defect prediction (SDP) is a critical endeavor for enshancing software reliability and optimizing quality assurance efforts by enabling the early identification of fault-prone components within the development lifecycle. This study presents a comprehensive empirical evaluation comparing the efficacy of classic...
In this study, a comprehensive evaluation of abstractive and extractive summarization performance across three prominent large language models (LLMs): ChatGPT, DeepSeek, and Gemini is presented. A total of 8,000 cardiovascular-related research abstracts were collected from PubMed and summarized using two distinct promp...
Burcu Baştürk, Aytuğ Onan· Sakarya University Journal o...· 0 citations
The results indicate that abstractive summaries, particularly those generated by Gemini and ChatGPT, achieve coherence levels comparable to or higher than original abstracts under LDA and LSA, while extractive summaries exhibit greater variability.
Findings indicate that integrating topic modeling– based thematic information with PLMs provides an effective, generalizable, and practical solution for biomedical MLTC tasks, and enriching general-purpose language models with topic information offers a promising alternative that reduces reliance on costly, data-intens...
Findings demonstrate that compact models can achieve strong biomedical classification performance through KD under compatible teacher–student pairings, while also highlighting that KD effectiveness varies substantially depending on the specific model combination.
