4 papers
MolEvolve: LLM-Guided Evolutionary Search for Interpretable Molecular Optimization
Xiangsen Chen, Ruilong Wu, Yanyan Lan +2
Despite deep learning's success in chemistry, its impact is hindered by a lack of interpretability and an inability to resolve activity cliffs, where minor structural nuances trigg…
CyberThreat-Eval: Can Large Language Models Automate Real-World Threat Research?
Xiangsen Chen, Xuan Feng, Shuo Chen +5
Analyzing Open Source Intelligence (OSINT) from large volumes of data is critical for drafting and publishing comprehensive CTI reports. This process usually follows a three-stage…
Review-Then-Refine: A Dynamic Framework for Multi-Hop Question Answering with Temporal Adaptability
Xiangsen Chen, Xuming Hu, Nan Tang
Retrieve-augmented generation (RAG) frameworks have emerged as a promising solution to multi-hop question answering(QA) tasks since it enables large language models (LLMs) to incor…
CRAG -- Comprehensive RAG Benchmark
Xiao Yang, Kai Sun, Hao Xin +24
Retrieval-Augmented Generation (RAG) has recently emerged as a promising solution to alleviate Large Language Model (LLM)'s deficiency in lack of knowledge. Existing RAG datasets,…