Papers by Yexiang Wang
Slot Attention with Value Normalization for Multi-Domain Dialogue State Tracking (2020.emnlp-main)
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| Challenge: | Existing dialogue state tracking approaches rely on ontology already defined, where all slots and their possible values are given. |
| Approach: | They propose a new architecture to exploit domain ontology by using Slot Attention and Value Normalization . they supplement the annotation of supporting span for MultiWOZ 2.1, which is the shortest span in utterances to support the labeled value. |
| Outcome: | The proposed architecture exploits ontology and can convert supporting spans to values. |
Reformulating Domain Adaptation of Large Language Models as Adapt-Retrieve-Revise: A Case Study on Chinese Legal Domain (2024.findings-acl)
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| Challenge: | Recent large language models like GPT-4 have demonstrated astonishing zero-shot capabilities in general domain tasks, but they often generate content with hallucinations in specific domains such as Chinese law. |
| Approach: | They propose a framework for adapting large language models (LLMs) to Chinese legal domains by reformulating generation as an adapt-retrieve-revise process. |
| Outcome: | The proposed framework outperforms existing models in the Chinese legal domain by +33.6 points in the zero-shot setting. |