Papers by Reynold Cheng
Before Generation, Align it! A Novel and Effective Strategy for Mitigating Hallucinations in Text-to-SQL Generation (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) driven by In-Context Learning (ICL) have improved performance of text-to-SQL. |
| Approach: | They propose a strategy to mitigate hallucinations in large language models driven by In-Context Learning (ICL) they propose TA-SQL, a text-to-Sql framework that encourages LLMs to take advantage of similar tasks rather than starting from scratch. |
| Outcome: | The proposed framework improves the performance of the GPT-4 model by 21.23% on BIRD dev. |
Unlocking Continual Learning Abilities in Language Models (2024.findings-emnlp)
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| Challenge: | Existing approaches to learning models (LMs) incorporate old task data or task-wise inductive bias into LMs, but old data and accurate task information are often unavailable or costly to collect. |
| Approach: | They propose a rehearsal-free method that updates model parameters with large magnitudes . they found that the L1-normalized magnitude distribution is different when different task data is used . |
| Outcome: | The proposed method improves accuracy and performance on four CL benchmarks. |
SHARE: An SLM-based Hierarchical Action CorREction Assistant for Text-to-SQL (2025.acl-long)
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| Challenge: | Existing approaches to self-correct text-to-SQL fail to demonstrate underlying reasoning path . authors propose **SHARE**, a self-revolution assistant for text-based error correction . |
| Approach: | They propose a "SHARE" assistant that enables LLMs to perform more precise error localization and efficient correction. |
| Outcome: | The proposed assistant performs more precise error localization and efficient correction for monolithic SQL queries. |
Micro-Act: Mitigate Knowledge Conflict in Question Answering via Actionable Self-Reasoning (2025.acl-long)
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| Challenge: | Existing approaches to mitigate knowledge conflict by comparing two knowledge sources can overwhelm LLMs with extraneous or lengthy contexts. |
| Approach: | They propose a framework that decomposes knowledge into fine-grained comparisons . they propose 'Micro-Act' framework that allows for reasoning beyond the superficial context . |
| Outcome: | The proposed framework achieves significant increase in QA accuracy over state-of-the-art baselines on five benchmark datasets. |