Summary of Aligning Large Language Models to a Domain-specific Graph Database For Nl2gql, by Yuanyuan Liang et al.
Aligning Large Language Models to a Domain-specific Graph Database for NL2GQL
by Yuanyuan Liang, Keren Tan, Tingyu Xie, Wenbiao Tao, Siyuan Wang, Yunshi Lan, Weining Qian
First submitted to arxiv on: 26 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Databases (cs.DB)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel pipeline to translate Natural Language (NL) into the Graph Query Language (GQL), tackling the challenges of NL2GQL in specific domains. The proposed approach utilizes ChatGPT to generate NL-GQL data pairs, leveraging self-instruction and fine-tuning Large Language Models (LLMs). Additionally, the paper highlights the importance of relevant schema for efficient GQL generation. Two datasets, FinGQL and MediGQL, are constructed from graph DBs in finance and medicine domains to evaluate the method’s performance. The results show significant improvements over baseline methods, with increases of 5.90-6.36 absolute points on EM and 6.00-7.09 absolute points on EX for both datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how we can use computers to communicate in a special way called Graph Query Language (GQL). Right now, it’s hard to turn words into GQL because it’s very specific and requires lots of information. The researchers created a new process using ChatGPT to help computers learn this language better. They also found that having the right “map” or schema helps make sure the computer gets things correct. To test their idea, they made two sets of examples from real-world data in finance and medicine. Their results show that their approach is much better than previous ways of doing it. |
Keywords
» Artificial intelligence » Fine tuning
