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arXiv:2603.00478v1 Announce Type: new Abstract: Few-shot transfer has been revolutionized by stronger pre-trained models and improved adaptation algorithms.However, there lacks a unified, rigorous evaluation protocol …
arXiv:2603.00481v1 Announce Type: new Abstract: Developments in the machine learning voting domain have shown both promising results and risks. Trained models perform well on ballot …
arXiv:2603.00488v1 Announce Type: new Abstract: Adolescent pornography addiction requires early detection based on objective neurobiological biomarkers because self-report is prone to subjective bias due to …
arXiv:2602.23546v1 Announce Type: new Abstract: Human reasoning often involves working over limited information to arrive at probabilistic conclusions. In its simplest form, this involves making …
arXiv:2602.23547v1 Announce Type: new Abstract: Sentences like "She will go to France or Spain, or perhaps to Germany or France." appear formally redundant, yet become …
arXiv:2602.23577v1 Announce Type: new Abstract: Suicide remains a pressing global public health concern. While social media platforms offer opportunities for early risk detection through online …
arXiv:2602.23580v1 Announce Type: new Abstract: In the field of educational assessment, automated scoring systems increasingly rely on deep learning and large language models (LLMs). However, …
arXiv:2602.23603v1 Announce Type: new Abstract: Long-form question answering (LFQA) demands nuanced evaluation of multi-sentence explanatory responses, yet existing metrics often fail to reflect human judgment. …
arXiv:2602.23610v1 Announce Type: new Abstract: The reasoning capability of large language models (LLMs), defined as their ability to analyze, infer, and make decisions based on …
arXiv:2602.23656v1 Announce Type: new Abstract: TRIZ-based contradiction mining is a fundamental task in patent analysis and systematic innovation, as it enables the identification of improving …
arXiv:2602.23729v1 Announce Type: new Abstract: The evaluation of large language models (LLMs) has predominantly relied on static datasets, which offer limited scalability and fail to …
arXiv:2602.23753v1 Announce Type: new Abstract: This study addresses the issues of semantic entanglement, unclear label structure, and insufficient feature representation in few-shot text classification, and …