Summary of Improving Embedding with Contrastive Fine-tuning on Small Datasets with Expert-augmented Scores, by Jun Lu et al.
Improving embedding with contrastive fine-tuning on small datasets with expert-augmented scores
by Jun Lu, David Li, Bill Ding, Yu Kang
First submitted to arxiv on: 19 Aug 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 proposed approach improves text embedding models by fine-tuning them on small datasets augmented with expert scores. This method focuses on enhancing semantic textual similarity tasks and addressing text retrieval problems. Soft labels derived from expert-augmented scores are used to fine-tune embedding models, ensuring their versatility is preserved while improving retrieval capability. The paper evaluates the method using a Q&A dataset from an online shopping website and eight expert models, showing improved performance over a benchmark model across multiple metrics on various retrieval tasks from the massive text embedding benchmark (MTEB). This cost-effective and practical approach can be applied to real-world scenarios where labeled data is scarce. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make computers better at understanding natural language. The researchers developed a way to improve text embeddings by training them on small datasets with expert input. This method works well for tasks like finding similar texts and answering questions. To test this approach, the team used a dataset from an online shopping website and compared their results to those of a benchmark model. They found that their method performed better across multiple metrics. This new technique is useful because it can be applied to real-world scenarios where there isn’t much labeled data. |
Keywords
» Artificial intelligence » Embedding » Fine tuning
