Search Results for author:
Found 11 papers, 7 papers with code
A Hierarchical Language Model For Interpretable Graph Reasoning
Large language models (LLMs) are being increasingly explored for graph tasks.
Geometry Informed Tokenization of Molecules for Language Model Generation
We consider molecule generation in 3D space using language models (LMs), which requires discrete tokenization of 3D molecular geometries.
Fragment and Geometry Aware Tokenization of Molecules for Structure-Based Drug Design Using Language Models
Benefiting from employing LMs with fragment-based generation and effective protein context encoding, our model achieves the best performance on binding vina score and chemical properties such as QED and Lipinski, which shows our model's efficacy in generating drug-like ligands with higher binding affinity against target proteins.
Derivative-Free Guidance in Continuous and Discrete Diffusion Models with Soft Value-Based Decoding
Diffusion models excel at capturing the natural design spaces of images, molecules, DNA, RNA, and protein sequences.
Eliminating Position Bias of Language Models: A Mechanistic Approach
Position bias has proven to be a prevalent issue of modern language models (LMs), where the models prioritize content based on its position within the given context.
Active Test-Time Adaptation: Theoretical Analyses and An Algorithm
Extensive experimental results confirm consistency with our theoretical analyses and show that the proposed ATTA method yields substantial performance improvements over TTA methods while maintaining efficiency and shares similar effectiveness to the more demanding active domain adaptation (ADA) methods.
TrustLLM: Trustworthiness in Large Language Models
This paper introduces TrustLLM, a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions.
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences.
Graph Structure and Feature Extrapolation for Out-of-Distribution Generalization
Out-of-distribution (OOD) generalization deals with the prevalent learning scenario where test distribution shifts from training distribution.
Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization
In this work, we propose to simultaneously incorporate label and environment causal independence (LECI) to fully make use of label and environment information, thereby addressing the challenges faced by prior methods on identifying causal and invariant subgraphs.
GOOD: A Graph Out-of-Distribution Benchmark
Our GOOD benchmark is a growing project and expects to expand in both quantity and variety of resources as the area develops.
