Papers by Izzeddin Gur
What It Takes to Achieve 100% Condition Accuracy on WikiSQL (D18-1)
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| Challenge: | despite of its simplicity, none of the publicly reported structured query generation models can achieve an accuracy beyond 62%, which is far from enough for practical use. |
| Approach: | They propose a model that can achieve 88.6% condition accuracy on WikiSQL . they ask: why is the accuracy still low for such simple queries? |
| Outcome: | The proposed solution can reach up to 88.6% condition accuracy on the WikiSQL dataset. |
DialSQL: Dialogue Based Structured Query Generation (P18-1)
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| Challenge: | Recent advances in deep learning and semantic parsing have improved the translation accuracy of natural language questions to structured queries. |
| Approach: | They propose a dialogue-based structured query generation framework that leverages human intelligence to boost performance of existing algorithms via user interaction. |
| Outcome: | The proposed framework improves on a WikiSQL dataset from 61.3% to 69.0% using only 2.4 validation questions per dialogue. |
Understanding HTML with Large Language Models (2023.findings-emnlp)
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Izzeddin Gur, Ofir Nachum, Yingjie Miao, Mustafa Safdari, Austin Huang, Aakanksha Chowdhery, Sharan Narang, Noah Fiedel, Aleksandra Faust
| Challenge: | Large language models have shown exceptional performance on a variety of natural language tasks, but their capabilities for HTML understanding have not been fully explored. |
| Approach: | They propose to use HTML understanding models to parse HTML and perform HTML navigation tasks with a large-scale HTML dataset. |
| Outcome: | The proposed models perform 50% more tasks with 192x less data than the previous best supervised model. |