6 citations · 20 across the 12 of their papers we have counts for
12 papers
Towards Diverse Scientific Hypothesis Search with Large Language Models
Haorui Wang, Parshin Shojaee, Kazem Meidani +7
Large language models (LLMs) are on the rise for accelerating scientific discovery, most recently in advanced tasks such as generating valid scientific hypotheses. Yet in many disc…
Accelerating Scientific Discovery with Autonomous Goal-evolving Agents
Yuanqi Du, Botao Yu, Tianyu Liu +25
There has been unprecedented interest in developing agents that expand the boundary of scientific discovery, primarily by optimizing quantitative objective functions specified by s…
Evaluating Large Language Models in Scientific Discovery
Zhangde Song, Jieyu Lu, Yuanqi Du +53
Large language models (LLMs) are increasingly applied to scientific research, yet prevailing science benchmarks probe decontextualized knowledge and overlook the iterative reasonin…
Two Birds with One Stone: Enhancing Uncertainty Quantification and Interpretability with Graph Functional Neural Process
Lingkai Kong, Haotian Sun, Yuchen Zhuang +3
Graph neural networks (GNNs) are powerful tools on graph data. However, their predictions are mis-calibrated and lack interpretability, limiting their adoption in critical applicat…
LLM-Augmented Chemical Synthesis and Design Decision Programs
Haorui Wang, Jeff Guo, Lingkai Kong +4
Retrosynthesis, the process of breaking down a target molecule into simpler precursors through a series of valid reactions, stands at the core of organic chemistry and drug develop…
MatLLMSearch: Crystal Structure Discovery with Evolution-Guided Large Language Models
Jingru Gan, Peichen Zhong, Yuanqi Du +7
Crystal structure generation is fundamental to materials science, enabling the discovery of novel materials with desired properties. While existing approaches leverage Large Langua…
PLaD: Preference-based Large Language Model Distillation with Pseudo-Preference Pairs
Rongzhi Zhang, Jiaming Shen, Tianqi Liu +7
Large Language Models (LLMs) have exhibited impressive capabilities in various tasks, yet their vast parameter sizes restrict their applicability in resource-constrained settings.…
Efficient Evolutionary Search Over Chemical Space with Large Language Models
Haorui Wang, Marta Skreta, Cher-Tian Ser +11
Molecular discovery, when formulated as an optimization problem, presents significant computational challenges because optimization objectives can be non-differentiable. Evolutiona…
Aligning Large Language Models with Representation Editing: A Control Perspective
Lingkai Kong, Haorui Wang, Wenhao Mu +7
Aligning large language models (LLMs) with human objectives is crucial for real-world applications. However, fine-tuning LLMs for alignment often suffers from unstable training and…
Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints
Lingkai Kong, Yuanqi Du, Wenhao Mu +8
Addressing real-world optimization problems becomes particularly challenging when analytic objective functions or constraints are unavailable. While numerous studies have addressed…
TPD: Enhancing Student Language Model Reasoning via Principle Discovery and Guidance
Haorui Wang, Rongzhi Zhang, Yinghao Li +4
Large Language Models (LLMs) have recently showcased remarkable reasoning abilities. However, larger models often surpass their smaller counterparts in reasoning tasks, posing the…
Assessing Logical Puzzle Solving in Large Language Models: Insights from a Minesweeper Case Study
Yinghao Li, Haorui Wang, Chao Zhang
Large Language Models (LLMs) have shown remarkable proficiency in language understanding and have been successfully applied to a variety of real-world tasks through task-specific f…