4 papers
The Interaction Bottleneck of Deep Neural Networks: Discovery, Proof, and Modulation
Huiqi Deng, Qihan Ren, Zhuofan Chen +5
Understanding what kinds of cooperative structures deep neural networks (DNNs) can represent remains a fundamental yet insufficiently understood problem. In this work, we treat int…
Attribution Explanations for Deep Neural Networks: A Theoretical Perspective
Huiqi Deng, Hongbin Pei, Quanshi Zhang +1
Attribution explanation is a typical approach for explaining deep neural networks (DNNs), inferring an importance or contribution score for each input variable to the final output.…
Deliberation on Priors: Trustworthy Reasoning of Large Language Models on Knowledge Graphs
Jie Ma, Ning Qu, Zhitao Gao +8
Knowledge graph-based retrieval-augmented generation seeks to mitigate hallucinations in Large Language Models (LLMs) caused by insufficient or outdated knowledge. However, existin…
Debate on Graph: a Flexible and Reliable Reasoning Framework for Large Language Models
Jie Ma, Zhitao Gao, Qi Chai +8
Large Language Models (LLMs) may suffer from hallucinations in real-world applications due to the lack of relevant knowledge. In contrast, knowledge graphs encompass extensive, mul…