Papers by Silei Xu
A Few-Shot Semantic Parser for Wizard-of-Oz Dialogues with the Precise ThingTalk Representation (2022.findings-acl)
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| Challenge: | Existing approaches to build effective semantic parsers for Wizard-of-Oz are insufficient. |
| Approach: | They propose a new dialogue representation and a sample-efficient methodology that can predict precise dialogue states in WOZ conversations. |
| Outcome: | The proposed model can predict precise dialogue states in WOZ conversations. |
AutoQA: From Databases To QA Semantic Parsers With Only Synthetic Training Data (2020.emnlp-main)
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| Challenge: | Existing methods to generate semantic parsers that answer questions on databases require large amounts of annotated data. |
| Approach: | They propose a method to generate semantic parsers that answer questions on databases . they use automatic paraphrasing and template-based parsing to find alternative expressions . |
| Outcome: | The proposed method achieves 69.8% answer accuracy on natural questions, 16.4% higher than state-of-the-art models and 5.2% lower than the same model trained with human data. |
WPO: Enhancing RLHF with Weighted Preference Optimization (2024.emnlp-main)
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Wenxuan Zhou, Ravi Agrawal, Shujian Zhang, Sathish Reddy Indurthi, Sanqiang Zhao, Kaiqiang Song, Silei Xu, Chenguang Zhu
| Challenge: | Off-policy preference optimization suffers from a distributional gap between the policy used for data collection and the target policy, leading to suboptimal optimization. |
| Approach: | They propose a method to simulate on-policy learning with off-police preference data. |
| Outcome: | The proposed method outperforms Direct Preference Optimization (DPO) by up to 5.6% on Alpaca Eval 2 and MT-bench. |
Localizing Open-Ontology QA Semantic Parsers in a Day Using Machine Translation (2020.emnlp-main)
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| Challenge: | a new toolkit for localizing a semantic parser for a language is proposed . the proposed approach is based on a method for question answering systems . |
| Approach: | They propose a toolkit that leverages Neural Machine Translation systems to localize a semantic parser for a new language. |
| Outcome: | The proposed approach outperforms state-of-the-art methods in 10 new languages . it can be deployed in restaurants and hotels in less than 24 hours . |
Fine-tuned LLMs Know More, Hallucinate Less with Few-Shot Sequence-to-Sequence Semantic Parsing over Wikidata (2023.emnlp-main)
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| Challenge: | Large language models can answer many questions correctly, but can also hallucinate and give wrong answers. |
| Approach: | They propose a question-answering benchmark for Wikidata that uses SPARQL to ground large language models. |
| Outcome: | The proposed method outperforms the state-of-the-art for QALD-7 by 3.6% in F1 score. |