28 citations · 29 across the 5 of their papers we have counts for
5 papers
Improved Logical Reasoning of Language Models via Differentiable Symbolic Programming
Hanlin Zhang, Jiani Huang, Ziyang Li +2
Pre-trained large language models (LMs) struggle to perform logical reasoning reliably despite advances in scale and compositionality. In this work, we tackle this challenge throug…
Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the MACHIAVELLI Benchmark
Alexander Pan, Jun Shern Chan, Andy Zou +7
Artificial agents have traditionally been trained to maximize reward, which may incentivize power-seeking and deception, analogous to how next-token prediction in language models (…
Fuzziness-tuned: Improving the Transferability of Adversarial Examples
Xiangyuan Yang, Jie Lin, Hanlin Zhang +2
With the development of adversarial attacks, adversairal examples have been widely used to enhance the robustness of the training models on deep neural networks. Although considera…
Improving the Robustness and Generalization of Deep Neural Network with Confidence Threshold Reduction
Xiangyuan Yang, Jie Lin, Hanlin Zhang +2
Deep neural networks are easily attacked by imperceptible perturbation. Presently, adversarial training (AT) is the most effective method to enhance the robustness of the model aga…
Stochastic Neural Networks with Infinite Width are Deterministic
Liu Ziyin, Hanlin Zhang, Xiangming Meng +3
This work theoretically studies stochastic neural networks, a main type of neural network in use. We prove that as the width of an optimized stochastic neural network tends to infi…