Papers by Ruiheng Liu

2 papers
Discarding the Crutches: Adaptive Parameter-Efficient Expert Meta-Learning for Continual Semantic Parsing (2025.coling-main)

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Challenge: Continual Semantic Parsing (CSP) enables parsers to generate SQL from natural language questions in task streams, using minimal annotated data to handle dynamically evolving databases in real-world scenarios.
Approach: They propose a Adaptive PET eXpert meta-learning approach that assists experts in adaptively warming up, ensuring better model initialization.
Outcome: The proposed method outperforms existing methods on two benchmarks and achieves superior performance without data replay or ideal settings.
SecureSQL: Evaluating Data Leakage of Large Language Models as Natural Language Interfaces to Databases (2024.findings-emnlp)

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Challenge: Existing studies on the vulnerability of large language models to SQL injection have been limited.
Approach: They propose to evaluate the potential of language models to leak sensitive data when generating SQL queries.
Outcome: The proposed model with the best performance has an accuracy of 61.7%, compared to humans who achieve 94% accuracy.

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