most citedGeoGalactica: A Scientific Large Language Model in Geoscience

17 citations · 43 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG2024★ 7 cited

DS-Agent: Automated Data Science by Empowering Large Language Models with Case-Based Reasoning

Siyuan Guo, Cheng Deng, Ying Wen +3

In this work, we investigate the potential of large language models (LLMs) based agents to automate data science tasks, with the goal of comprehending task requirements, then build…

cs.CL2024★ 17 cited

GeoGalactica: A Scientific Large Language Model in Geoscience

Zhouhan Lin, Cheng Deng, Le Zhou +18

Large language models (LLMs) have achieved huge success for their general knowledge and ability to solve a wide spectrum of tasks in natural language processing (NLP). Due to their…

cs.CL2023

Enhancing Uncertainty-Based Hallucination Detection with Stronger Focus

Tianhang Zhang, Lin Qiu, Qipeng Guo +6

Large Language Models (LLMs) have gained significant popularity for their impressive performance across diverse fields. However, LLMs are prone to hallucinate untruthful or nonsens…

cs.CL2023★ 14 cited

K2: A Foundation Language Model for Geoscience Knowledge Understanding and Utilization

Cheng Deng, Tianhang Zhang, Zhongmou He +9

Large language models (LLMs) have achieved great success in general domains of natural language processing. In this paper, we bring LLMs to the realm of geoscience with the objecti…

cs.IR2023★ 3 cited

Covidia: COVID-19 Interdisciplinary Academic Knowledge Graph

Cheng Deng, Jiaxin Ding, Luoyi Fu +3

The pandemic of COVID-19 has inspired extensive works across different research fields. Existing literature and knowledge platforms on COVID-19 only focus on collecting papers on b…

cs.LG2023★ 2 cited

FMGNN: Fused Manifold Graph Neural Network

Cheng Deng, Fan Xu, Jiaxing Ding +3

Graph representation learning has been widely studied and demonstrated effectiveness in various graph tasks. Most existing works embed graph data in the Euclidean space, while rece…

cs.CL2023

PK-Chat: Pointer Network Guided Knowledge Driven Generative Dialogue Model

Cheng Deng, Bo Tong, Luoyi Fu +4

In the research of end-to-end dialogue systems, using real-world knowledge to generate natural, fluent, and human-like utterances with correct answers is crucial. However, domain-s…