MrezaPourreza

4 ms·

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by MrezaPourreza 3y ago
Rather than converting the entirety of structured data into unstructured text, we opt to provide the language model with sample rows and the database schema. Utilizing this approach, the LLM is then tasked with generating a SQL query to add
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by MrezaPourreza 3y ago
Yes, based on what I've experimented with OpenAI's assistants, it still requires engineering and developing tools to get the best performance on large databases.
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by MrezaPourreza 3y ago
Hello, thank you very much for your meticulous comment. The 85.3% accuracy reported in our paper (I'm one of the authors of the DIN-SQL paper) pertains to the test set. However, in the blog post, we are reporting the performance on the
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by MrezaPourreza 3y ago
Yes, we have already submitted the model for evaluation on the Spider holdout test set. While your suggestion is certainly intriguing, implementing a universal solution could be quite challenging, as it would heavily depend on the specifics
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by MrezaPourreza 3y ago
I believe this article underscores the significance and efficacy of fine-tuning for specific tasks. Looking ahead, I envision the integration of fine-tuned models with RAG agents and models, further enhancing overall performance.
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A tutorial on fine-tuning GPT-3.5-turbo for Natural language to SQL(medium.com)
3 points by MrezaPourreza 3y ago|1 comments
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by MrezaPourreza 3y ago
A tutorial on how to fine-tune a GPT3.5 model for Natural Language to SQL tasks and a comparison of its performance vs Retrieval Augmented Generation. Based on the results, fine-tuning can match and outperform RAG (the approach matches the
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by MrezaPourreza 3y ago
Hello, and thank you for your positive feedback on our work with Dataherald. I'm currently in the process of tidying up the DIN-SQL repository, and I apologize for any inconvenience this may cause :). Regarding the usage of other Large
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by MrezaPourreza 3y ago
Thank you for your interest in our work. The schema linking approach employed in our agent significantly differs from the one described in my paper. In the paper, we utilized a method that involved breaking down questions and matching entit
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by MrezaPourreza 3y ago
For schema matching, we leverage embeddings. We create embeddings for the tables within the database and generate one for the natural language question provided. We then calculate the cosine similarity between these embeddings to determine
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by MrezaPourreza 3y ago
I'd be delighted to assist you with your inquiries. Indeed, the context store interacts with vector databases to retrieve samples based on vector embeddings and cosine similarity.
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Evaluate LLMs Generated SQL(medium.com)
2 points by MrezaPourreza 3y ago|1 comments
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by MrezaPourreza 3y ago
This link contains some methods to get a confidence score for SQL queries.