Papers by Yibing Zhan
TASA: Deceiving Question Answering Models by Twin Answer Sentences Attack (2022.emnlp-main)
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| Challenge: | Existing adversarial models rely on keyword matching and ignore relevant contextual relations for answer prediction. |
| Approach: | They propose to use keyword matching to attack model with two biases that rely on a perturbed answer sentence and a distracting answer sentence to misguide model. |
| Outcome: | The proposed method produces fluent and grammatical adversarial contexts while maintaining gold answers. |
Divide, Conquer, and Combine: Mixture of Semantic-Independent Experts for Zero-Shot Dialogue State Tracking (2023.acl-long)
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| Challenge: | Existing methods to enhance the zeroshot generalization of DST fail to effectively decouple semantics of samples, limiting the zero-shot performance of the system. |
| Approach: | They propose a new learning schema that explicitly disentangles the semantics of seen data and leverages the performance and robustness with the mixture-of-experts mechanism. |
| Outcome: | The proposed model achieves state-of-the-art on multiWOZ2.1 with 10M trainable parameters and is robust to the mixture-of experts mechanism. |
Large Language Models as an Indirect Reasoner: Contrapositive and Contradiction for Automated Reasoning (2025.coling-main)
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| Challenge: | Recent studies have focused on improving the ability of Large Language Models to perform complex reasoning. |
| Approach: | They propose a Direct-Indirect Reasoning method that integrates DR and IR as parallel reasoning paths that are merged to derive the final answer. |
| Outcome: | The proposed method outperforms existing methods on four datasets related to logical reasoning and proof. |