36 citations · 49 across the 20 of their papers we have counts for
5 papers · 1 filter
Accurate, Efficient, and Explainable Deep Learning Approaches for Environmental Science Problems
Jimeng Shi
Environmental science plays a pivotal role in safeguarding ecosystems, a domain driven by large-scale, heterogeneous data. In the big data era, artificial intelligence (AI) has eme…
Retrieval is Cheap, Show Me the Code: Executable Multi-Hop Reasoning for Retrieval-Augmented Generation
Jiashuo Sun, Jimeng Shi, Yixuan Xie +10
Retrieval-Augmented Generation (RAG) has become a standard approach for knowledge-intensive question answering, but existing systems remain brittle on multi-hop questions, where so…
TaSR-RAG: Taxonomy-guided Structured Reasoning for Retrieval-Augmented Generation
Jiashuo Sun, Yixuan Xie, Jimeng Shi +2
Retrieval-Augmented Generation (RAG) helps large language models (LLMs) answer knowledge-intensive and time-sensitive questions by conditioning generation on external evidence. How…
Any Model, Any Place, Any Time: Get Remote Sensing Foundation Model Embeddings On Demand
Dingqi Ye, Daniel Kiv, Wei Hu +2
The remote sensing community is witnessing a rapid growth of foundation models, which provide powerful embeddings for a wide range of downstream tasks. However, practical adoption…
MultiCube-RAG for Multi-hop Question Answering
Jimeng Shi, Wei Hu, Runchu Tian +8
Multi-hop question answering (QA) necessitates multi-step reasoning and retrieval across interconnected subjects, attributes, and relations. Existing retrieval-augmented generation…