Hybrid search strategy (WOS and LLM-assisted) for lettuce (Lactuca sativa L.) photopriming and drought tolerance response. (Web of Science Core Collection).
Abstract This search strategy represents a hybrid methodology combining Artificial Intelligence (AI) semantic retrieval and database validation. Initially, Large Language Models (LLMs)-specifically OpenAI's ChatGPT and Google's Gemini-were queried using standardized natural language prompts focused on the intersection of 'photopriming', 'LED spectra (red/far-red)', 'stress memory', and 'drought tolerance' in lettuce ( Lactuca sativa L.). The specific article links and digital object identifiers (DOIs) retrieved by the AI models were subsequently cross-referenced, validated, and filtered within the Web of Science (WOS) Core Collection platform to guarantee formal indexing, impact factor, and literature eligibility. This record stores the definitive set of verified DOIs that constitutes the bibliographical baseline for the experimental study.
This paper investigates developing real-world perception for active agents, proposes Gibson Environment for this purpose, and showcases a set of perceptual tasks learned therein.
F. Xia, Amir Zamir, Zhi-Yang He et al.· 2018 IEEE/CVF Conference on...· 1,069 citations· ⚡112
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
An algorithm is proposed which leverages disentangled semantic factors to generate adversarial perturbation by altering controlled semantic attributes to fool the learner towards various "adversarial" targets.
Haonan Qiu, Chaowei Xiao, Lei Yang et al.· arXiv.org· 208 citations· ⚡17
This work proposes a HALO that directly maximizes the utility of generations instead of maximizing the log-likelihood of preferences, and suggests that the best loss depends on the inductive biases most appropriate for a given setting, an oft-overlooked consideration.
The results are packaged in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible, and the need to shorten time-to-market, by speeding up the development through low-precision engineering activities.
Carmine Giardino, Nicolò Paternoster, M. Unterkalmsteiner et al.· IEEE Transactions on Softwar...· 178 citations· ⚡14
Software startup companies develop innovative, software-intensive products within limited timeframes and with few resources, searching for sustainable and scalable business models.
M. Unterkalmsteiner, P. Abrahamsson, Xiaofeng Wang et al.· e-Informatica Software Engin...· 157 citations· ⚡17
