Tootfinder

Assessing GPTZero's Accuracy in Identifying AI vs. Human-Written Essays
Selin Dik, Osman Erdem, Mehmet Dik
https://arxiv.org/abs/2506.23517 https://

Assessing GPTZero's Accuracy in Identifying AI vs. Human-Written Essays
As the use of AI tools by students has become more prevalent, instructors have started using AI detection tools like GPTZero and QuillBot to detect AI written text. However, the reliability of these detectors remains uncertain. In our study, we focused mostly on the success rate of GPTZero, the most-used AI detector, in identifying AI-generated texts based on different lengths of randomly submitted essays: short (40-100 word count), medium (100-350 word count), and long (350-800 word count). We…

GenRecal: Generation after Recalibration from Large to Small Vision-Language Models
Byung-Kwan Lee, Ryo Hachiuma, Yong Man Ro, Yu-Chiang Frank Wang, Yueh-Hua Wu
https://arxiv.org/abs/2506.15681

GenRecal: Generation after Recalibration from Large to Small Vision-Language Models
Recent advancements in vision-language models (VLMs) have leveraged large language models (LLMs) to achieve performance on par with closed-source systems like GPT-4V. However, deploying these models in real-world scenarios, particularly on resource-constrained devices, remains challenging due to their substantial computational demands. This has spurred interest in distilling knowledge from large VLMs into smaller, more efficient counterparts. A key challenge arises here from the diversity of VL…

Optimizing Web-Based AI Query Retrieval with GPT Integration in LangChain A CoT-Enhanced Prompt Engineering Approach
Wenqi Guan, Yang Fang
https://arxiv.org/abs/2506.15512

Optimizing Web-Based AI Query Retrieval with GPT Integration in LangChain A CoT-Enhanced Prompt Engineering Approach
Large Language Models have brought a radical change in the process of remote learning students, among other aspects of educative activities. Current retrieval of remote learning resources lacks depth in contextual meaning that provides comprehensive information on complex student queries. This work proposes a novel approach to enhancing remote learning retrieval by integrating GPT-based models within the LangChain framework. We achieve this system in a more intuitive and productive manner using…

Private GPTs for LLM-driven testing in software development and machine learning
Jakub Jagielski, Markus Abel
https://arxiv.org/abs/2506.06509 https://

Private GPTs for LLM-driven testing in software development and machine learning
In this contribution, we examine the capability of private GPTs to automatically generate executable test code based on requirements. More specifically, we use acceptance criteria as input, formulated as part of epics, or stories, which are typically used in modern development processes. This gives product owners, or business intelligence, respectively, a way to directly produce testable criteria through the use of LLMs. We explore the quality of the so-produced tests in two ways: i) directly b…