Haoyi Zhou


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Path Spuriousness-aware Reinforcement Learning for Multi-Hop Knowledge Graph Reasoning
Chunyang Jiang | Tianchen Zhu | Haoyi Zhou | Chang Liu | Ting Deng | Chunming Hu | Jianxin Li
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

Multi-hop reasoning, a prevalent approach for query answering, aims at inferring new facts along reasonable paths over a knowledge graph.Reinforcement learning methods can be adopted by formulating the problem into a Markov decision process.However, common suffering within RL-based reasoning models is that the agent can be biased to spurious paths which coincidentally lead to the correct answer with poor explanation.In this work, we take a deep dive into this phenomenon and define a metric named Path Spuriousness (PS), to quantitatively estimate to what extent a path is spurious.Guided by the definition of PS, we design a model with a new reward that considers both answer accuracy and path reasonableness.We test our method on four datasets and experiments reveal that our method considerably enhances the agent’s capacity to prevent spurious paths while keeping comparable to state-of-the-art performance.


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THE-X: Privacy-Preserving Transformer Inference with Homomorphic Encryption
Tianyu Chen | Hangbo Bao | Shaohan Huang | Li Dong | Binxing Jiao | Daxin Jiang | Haoyi Zhou | Jianxin Li | Furu Wei
Findings of the Association for Computational Linguistics: ACL 2022

As more and more pre-trained language models adopt on-cloud deployment, the privacy issues grow quickly, mainly for the exposure of plain-text user data (e.g., search history, medical record, bank account). Privacy-preserving inference of transformer models is on the demand of cloud service users. To protect privacy, it is an attractive choice to compute only with ciphertext in homomorphic encryption (HE). However, enabling pre-trained models inference on ciphertext data is difficult due to the complex computations in transformer blocks, which are not supported by current HE tools yet. In this work, we introduce THE-X, an approximation approach for transformers, which enables privacy-preserving inference of pre-trained models developed by popular frameworks. THE-X proposes a workflow to deal with complex computation in transformer networks, including all the non-polynomial functions like GELU, softmax, and LayerNorm. Experiments reveal our proposed THE-X can enable transformer inference on encrypted data for different downstream tasks, all with negligible performance drop but enjoying the theory-guaranteed privacy-preserving advantage.