Summary of A Little Human Data Goes a Long Way, by Dhananjay Ashok and Jonathan May
A Little Human Data Goes A Long Way
by Dhananjay Ashok, Jonathan May
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper investigates the use of synthetic data in Fact Verification (FV) and Question Answering (QA) tasks. Synthetic data generation shows promise as a cost-effective alternative to human annotation, but its limitations are unclear. The study examines the effects of incrementally replacing human-generated data with synthetic points on eight diverse datasets. Surprisingly, replacing up to 90% of the training data only slightly decreases performance, while replacing the final 10% leads to significant declines. Models trained solely on synthetic data can be reliably improved by including a small number of human-generated data points. The results suggest that even when human annotation is infeasible at scale, having a small proportion of the dataset being human generated can be valuable. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at using fake data to train machines to verify facts and answer questions. Using fake data instead of real human-generated data seems promising, but how much it can replace human work is unclear. The researchers tested this idea by gradually replacing human-generated data with fake points on many different datasets. They found that even if they used most of the training data as fake, it only slightly hurt performance. But if they replaced almost all of the remaining data, it really mattered. They also showed that adding just a few real human-generated data points to synthetic data can greatly improve model performance. Overall, this study suggests that even when it’s hard or expensive to get lots of real human work done, having a little bit of high-quality real data can still be valuable. |
Keywords
» Artificial intelligence » Question answering » Synthetic data
