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20222026
most citedDARWIN Series: Domain Specific Large Language Models for Natural Science

33 citations · 71 across the 14 of their papers we have counts for

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Showing 2024Show all

5 papers · 1 filter

cs.CL2024

ByteScience: Bridging Unstructured Scientific Literature and Structured Data with Auto Fine-tuned Large Language Model in Token Granularity

Tong Xie, Hanzhi Zhang, Shaozhou Wang +5

Natural Language Processing (NLP) is widely used to supply summarization ability from long context to structured information. However, extracting structured knowledge from scientif…

cs.CL2024★ 4 cited

DARWIN 1.5: Large Language Models as Materials Science Adapted Learners

Tong Xie, Yuwei Wan, Yixuan Liu +8

Materials discovery and design aim to find compositions and structures with desirable properties over highly complex and diverse physical spaces. Traditional solutions, such as hig…

cs.CL2024★ 2 cited

From Tokens to Materials: Leveraging Language Models for Scientific Discovery

Yuwei Wan, Tong Xie, Nan Wu +3

Exploring the predictive capabilities of language models in material science is an ongoing interest. This study investigates the application of language model embeddings to enhance…

cs.CL2024★ 5 cited

SciQAG: A Framework for Auto-Generated Science Question Answering Dataset with Fine-grained Evaluation

Yuwei Wan, Yixuan Liu, Aswathy Ajith +6

We introduce SciQAG, a novel framework for automatically generating high-quality science question-answer pairs from a large corpus of scientific literature based on large language…

cs.CL2024★ 5 cited

Construction and Application of Materials Knowledge Graph in Multidisciplinary Materials Science via Large Language Model

Yanpeng Ye, Jie Ren, Shaozhou Wang +6

Knowledge in materials science is widely dispersed across extensive scientific literature, posing significant challenges to the efficient discovery and integration of new materials…