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Found 12 papers, 1 papers with code
DARWIN Series: Domain Specific Large Language Models for Natural Science
To add new capabilities in natural science, enabling the acceleration and enrichment of automation of the discovery process, we present DARWIN, a series of tailored LLMs for natural science, mainly in physics, chemistry, and material science.
Large Language Models as Master Key: Unlocking the Secrets of Materials Science with GPT
The amount of data has growing significance in exploring cutting-edge materials and a number of datasets have been generated either by hand or automated approaches.
Interdisciplinary Discovery of Nanomaterials Based on Convolutional Neural Networks
The material science literature contains up-to-date and comprehensive scientific knowledge of materials.
Multi-choice Relational Reasoning for Machine Reading Comprehension
We propose a multi-choice relational reasoning (McR$^2$) model with an aim to enable relational reasoning on candidates based on fusion representations of document, query and candidates.
Chinese Word Segmentation: Another Decade Review (2007-2017)
This paper reviews the development of Chinese word segmentation (CWS) in the most recent decade, 2007-2017.
Integrative Semantic Dependency Parsing via Efficient Large-scale Feature Selection
Semantic parsing, i. e., the automatic derivation of meaning representation such as an instantiated predicate-argument structure for a sentence, plays a critical role in deep processing of natural language.
