Summary of Auto-cypher: Improving Llms on Cypher Generation Via Llm-supervised Generation-verification Framework, by Aman Tiwari et al.
Auto-Cypher: Improving LLMs on Cypher generation via LLM-supervised generation-verification framework
by Aman Tiwari, Shiva Krishna Reddy Malay, Vikas Yadav, Masoud Hashemi, Sathwik Tejaswi Madhusudhan
First submitted to arxiv on: 17 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Machine Learning (cs.LG)
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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 research paper presents an automated pipeline to generate high-quality synthetic data for generating Cypher queries for Neo4j. The approach, LLM-Supervised, uses large language models like LLaMa-3.1-8B, Mistral-7B, and QWEN-7B to train on synthetic data, resulting in performance gains of up to 40% on the Text2Cypher test split and 30% on the SPIDER benchmark adapted for graph databases. The pipeline introduces a novel strategy called LLM-As-Database-Filler to ensure Cypher query correctness. The generated data, SynthCypher, contains 29.8k instances across various domains and queries with varying complexities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper creates a way to make computers generate good quality code for a special kind of database called Neo4j. Right now, people have to write this code by hand, which can be hard. The researchers used big computer models to learn how to generate this code, and it worked really well! They tested their idea on some challenges and it did better than expected. This could help make it easier for people to work with Neo4j databases in the future. |
Keywords
» Artificial intelligence » Llama » Supervised » Synthetic data
