23 citations · 27 across the 4 of their papers we have counts for
5 papers
Mephisto: Self-Improving Large Language Model-Based Agents for Automated Interpretation of Multi-band Galaxy Observations
Zechang Sun, Yuan-Sen Ting, Yaobo Liang +3
Astronomical research has long relied on human expertise to interpret complex data and formulate scientific hypotheses. In this study, we introduce Mephisto -- a multi-agent collab…
Interpreting Multi-band Galaxy Observations with Large Language Model-Based Agents
Zechang Sun, Yuan-Sen Ting, Yaobo Liang +3
Astronomical research traditionally relies on extensive domain knowledge to interpret observations and narrow down hypotheses. We demonstrate that this process can be emulated usin…
Knowledge Graph in Astronomical Research with Large Language Models: Quantifying Driving Forces in Interdisciplinary Scientific Discovery
Zechang Sun, Yuan-Sen Ting, Yaobo Liang +3
Identifying and predicting the factors that contribute to the success of interdisciplinary research is crucial for advancing scientific discovery. However, there is a lack of metho…
PPTC-R benchmark: Towards Evaluating the Robustness of Large Language Models for PowerPoint Task Completion
Zekai Zhang, Yiduo Guo, Yaobo Liang +2
The growing dependence on Large Language Models (LLMs) for finishing user instructions necessitates a comprehensive understanding of their robustness to complex task completion in…
Low-code LLM: Graphical User Interface over Large Language Models
Yuzhe Cai, Shaoguang Mao, Wenshan Wu +10
Utilizing Large Language Models (LLMs) for complex tasks is challenging, often involving a time-consuming and uncontrollable prompt engineering process. This paper introduces a nov…