Summary of Flex: Expert-level False-less Execution Metric For Reliable Text-to-sql Benchmark, by Heegyu Kim et al.
FLEX: Expert-level False-Less EXecution Metric for Reliable Text-to-SQL Benchmark
by Heegyu Kim, Taeyang Jeon, Seunghwan Choi, Seungtaek Choi, Hyunsouk Cho
First submitted to arxiv on: 24 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 paper tackles the critical issue of evaluating text-to-SQL systems accurately. The current most prevalent metric, Execution Accuracy (EX), has many false positives and negatives. To address this challenge, the authors introduce FLEX, a novel approach that utilizes large language models (LLMs) to emulate human expert-level evaluation of SQL queries. This new metric significantly improves agreement with human experts, increasing Cohen’s kappa from 62 to 87.04. The paper also highlights several key insights: average model performance increases by over 2.6 points, annotation quality issues contribute to underestimation in EX, and model performance on challenging questions tends to be overestimated. This work has the potential to reshape our understanding of state-of-the-art performance in text-to-SQL systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure that computers can understand what we mean when we ask them to do something with data. Right now, there are a lot of ways to test how good these computer programs are, but some of those tests aren’t very accurate. The authors of this paper created a new way to test these programs called FLEX. It’s like having a super-smart person help you figure out if the program is doing things correctly. This new method makes it easier to compare different programs and see which ones are the best. The authors also found some interesting things about how well the programs work, like that they tend to do better on easy questions than hard ones. |
