Papers by Ruiqi Gao
Large Language Models are Limited in Out-of-Context Knowledge Reasoning (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) possess extensive knowledge and strong capabilities in performing in-context reasoning. |
| Approach: | They evaluated a dataset with seven representative OCKR tasks to assess their OCKr capabilities. |
| Outcome: | The model's OCKR abilities are limited regardless of whether the knowledge is trained in a separate or adjacent training setting. |
Enhancing Text-to-SQL Parsing through Question Rewriting and Execution-Guided Refinement (2024.findings-acl)
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| Challenge: | Existing prompt engineering methods exploit database content and execution feedback to improve text-to-sql performance. |
| Approach: | They propose a framework for large language model-based text-to-sql task that exploits database content and execution feedback to improve execution accuracy. |
| Outcome: | The proposed framework improves execution accuracy and usability by 12.41% and 5.38% on four widely used benchmarks. |