Pre-trained language models (PLMs) are first trained on a large dataset and then directly transferred to downstream tasks, or further fine-tuned on another small dataset for specific NLP tasks. Early PLMs, such as Skip-Gram [1] and GloVe [2], are shallow neural networks, and their word e...
In: IEEE International Conference on Big Data, BigData 2023, Sorrento, Italy, December 15-18, 2023, pp. 1236–1241 (2023) Li, Z., Zhu, H., Lu, Z., Yin, M.: Synthetic data generation with large language models for text classification: Potential and limitations. In: Proceedings of ...
(1) A white car and a red sheep (1) A single clock is sitting on a table (2) A panda making latte art (2) An umbrella on top of a spoon (3) A small red ball in a big green block (3) Wolf in a suit (4) A burning fish (4...
Bender E M, Gebru T, McMillan-Major A, Shmitchell S. On the dangers of stochastic parrots: Can language models be too big? InProc. the 2021 ACM Conference on Fairness, Accountability, and Transparency, Mar. 2021, pp.610–623. DOI:https://doi.org/10.1145/3442188.3445922. ChapterGoogle Sch...
Finding useful information from big data is very difficult and time consuming. Recommendation systems are a good solution to find useful information according to users' interests. Usually, recommendation system is a collection of algorithms that discover data patterns from the accessible dataset by ...
Deep learning models can accurately predict molecular properties and help making the search for potential drug candidates faster and more efficient. Many existing methods are purely data driven, focusing on exploiting the intrinsic topology and construction rules of molecules without any chemical prior inf...
AIM AND SCOPE In today's world, we are aware that how breakthroughs in data analytics and high-performance computing has made society-changing AI applications in different areas. One particular stand out success relates to learning from a massive amount of data in real time to quickly identify ...
Relying solely on data-driven models for automated vulnerability assessments can be restrictive, as these models are limited to the vulnerabilities they have been trained on. By utilizing a neurosymbolic approach, safety can be improved. In this method, experts simulate adversarial roles during the ...
Schema framework responsible for semantic enhancement of property graphs, such as subject models, evolutionary models, predicate models, etc. SPG-Builder knowledge construction Supports the construction of both structured and unstructured knowledge. Compatible and articulated with big data architecture, provi...
few-shot learningClassification methods based on fine-tuning pre-trained language models often require a large number of labeled samples; therefore, few-shot text classification has attracted considerable attention. Prompt learning is an effective method for addressing few-shot text classification tasks ...