Summary of Quality Matters: Evaluating Synthetic Data For Tool-using Llms, by Shadi Iskander et al.
Quality Matters: Evaluating Synthetic Data for Tool-Using LLMs
by Shadi Iskander, Nachshon Cohen, Zohar Karnin, Ori Shapira, Sofia Tolmach
First submitted to arxiv on: 24 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Software Engineering (cs.SE)
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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 A novel approach to evaluating the reliability of synthetic data for training large language models (LLMs) is presented, which addresses the current lack of systematic data quality checks. Two methods are proposed: human-defined correctness criteria and model-driven assessment with in-context evaluation. The effectiveness of these approaches is demonstrated through thorough evaluation on two popular benchmarks and an extrinsic evaluation showcasing the impact of data quality on model performance. Notably, models trained on high-quality data outperform those trained on unvalidated data, even when trained with a smaller quantity of data. This study empirically highlights the importance of assessing and ensuring the reliability of training data for tool-using LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are used to help us perform tasks outside of their original purpose. To make sure these models work well, we need good data to train them. Right now, there’s no way to check if this data is accurate or not. In this study, two new methods are suggested to solve this problem. One method uses people to define what makes the data correct, and the other method uses the model itself to evaluate its quality. The researchers tested these approaches on popular datasets and found that models trained with high-quality data do better than those trained with low-quality data, even if they use less data overall. This study shows why it’s important to make sure training data is reliable for large language models. |
Keywords
» Artificial intelligence » Synthetic data
