Publications (13)
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…
Large Language Models as Master Key: Unlocking the Secrets of Materials Science with GPT
Tong Xie, Yuwei Wan, Wei Huang +8
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. However, the ma…
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…
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…
MiST: Understanding the Role of Mid-Stage Scientific Training in Developing Chemical Reasoning Models
Andres M Bran, Tong Xie, Shai Pranesh +9
Large Language Models can develop reasoning capabilities through online fine-tuning with rule-based rewards. However, recent studies reveal a critical constraint: reinforcement lea…
DrugMCTS: a drug repurposing framework combining multi-agent, RAG and Monte Carlo Tree Search
Zerui Yang, Yuwei Wan, Siyu Yan +4
Recent advances in large language models have demonstrated considerable potential in scientific domains such as drug repositioning. However, their effectiveness remains constrained…