Defending Jailbreak Prompts via In-Context Adversarial Game

no code implementations • 20 Feb 2024 • Yujun Zhou, Yufei Han, Haomin Zhuang, Taicheng Guo, Kehan Guo, Zhenwen Liang, Hongyan Bao, Xiangliang Zhang

Large Language Models (LLMs) demonstrate remarkable capabilities across diverse applications.

SceMQA: A Scientific College Entrance Level Multimodal Question Answering Benchmark

no code implementations • 6 Feb 2024 • Zhenwen Liang, Kehan Guo, Gang Liu, Taicheng Guo, Yujun Zhou, Tianyu Yang, Jiajun Jiao, Renjie Pi, Jipeng Zhang, Xiangliang Zhang

The paper introduces SceMQA, a novel benchmark for scientific multimodal question answering at the college entrance level.

Multiple-choice Question Answering

Modeling non-uniform uncertainty in Reaction Prediction via Boosting and Dropout

no code implementations • 7 Oct 2023 • Taicheng Guo, Changsheng Ma, Xiuying Chen, Bozhao Nan, Kehan Guo, Shichao Pei, Nitesh V. Chawla, Olaf Wiest, Xiangliang Zhang

With the widespread adoption of generative models, the Variational Autoencoder(VAE) framework has typically been employed to tackle challenges in reaction prediction, where the reactants are encoded as a condition for the decoder, which then generates the product.

What can Large Language Models do in chemistry? A comprehensive benchmark on eight tasks

1 code implementation • NeurIPS 2023 • Taicheng Guo, Kehan Guo, Bozhao Nan, Zhenwen Liang, Zhichun Guo, Nitesh V. Chawla, Olaf Wiest, Xiangliang Zhang

In this paper, rather than pursuing state-of-the-art performance, we aim to evaluate capabilities of LLMs in a wide range of tasks across the chemistry domain.

In-Context Learning

Graph-based Molecular Representation Learning

1 code implementation • 8 Jul 2022 • Zhichun Guo, Kehan Guo, Bozhao Nan, Yijun Tian, Roshni G. Iyer, Yihong Ma, Olaf Wiest, Xiangliang Zhang, Wei Wang, Chuxu Zhang, Nitesh V. Chawla

Recently, MRL has achieved considerable progress, especially in methods based on deep molecular graph learning.

Graph Learning molecular representation +2