Summary of Gistembed: Guided In-sample Selection Of Training Negatives For Text Embedding Fine-tuning, by Aivin V. Solatorio
GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning
by Aivin V. Solatorio
First submitted to arxiv on: 26 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 development of embedding models is crucial for various AI applications, including semantic search and personalized recommendations. However, the scarcity of high-quality training data necessitates the use of automated methods to ensure data integrity. Traditional unsupervised triplet mining can automate training data generation but may introduce biases and noise, degrading model performance. To address this issue, a novel strategy called GISTEmbed is introduced, which enhances in-batch negative selection during contrastive training through a guide model. This approach significantly reduces noise from data quality issues and improves model fine-tuning. Compared to the Massive Text Embedding Benchmark (MTEB), GISTEmbed shows consistent performance improvements across various model sizes and achieves state-of-the-art results in select categories. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI models need good training data to work well, but collecting this data can be difficult. Researchers have been trying to find ways to automate the process of generating this data. However, these methods can sometimes introduce errors or biases that make the AI models less effective. A new approach called GISTEmbed has been developed to solve this problem. It improves the quality of the training data by using a guide model to help select the best examples for the AI model to learn from. This results in better-performing AI models and can even be used to create smaller, more efficient models that are less resource-intensive. |
Keywords
* Artificial intelligence * Embedding * Fine tuning * Unsupervised
