Summary of The Effects Of Hallucinations in Synthetic Training Data For Relation Extraction, by Steven Rogulsky et al.
The Effects of Hallucinations in Synthetic Training Data for Relation Extraction
by Steven Rogulsky, Nicholas Popovic, Michael Färber
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: This paper investigates the effects of generative data augmentation (GDA) on relation extraction in text data. GDA is a common approach to expand datasets for training and evaluating relation extraction models, but it often introduces hallucinations, or spurious facts, which can compromise model performance. The study finds that hallucinations significantly reduce recall rates between 19.1% and 39.2%, with relevant hallucinations having a greater impact than irrelevant ones. To address this issue, the authors develop methods for detecting hallucinations and improving data quality, achieving high F1-scores of 83.8% and 92.2%. The paper’s findings highlight the importance of addressing hallucinations in GDA to ensure accurate relation extraction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This research paper looks at how a technique called generative data augmentation (GDA) affects the ability of computers to extract relationships from text. GDA is a way to make datasets bigger and better, but it can also add fake information that can mess up the results. The study found that this fake information makes it harder for computers to find real relationships in the text, with some models performing as poorly as 19-40% worse than usual. To fix this problem, the researchers came up with ways to detect and remove this fake information, which worked really well. Overall, the paper shows how important it is to deal with fake information when trying to get computers to understand relationships in text. |
Keywords
» Artificial intelligence » Data augmentation » Recall
