Summary of Towards Understanding Domain Adapted Sentence Embeddings For Document Retrieval, by Sujoy Roychowdhury et al.
Towards Understanding Domain Adapted Sentence Embeddings for Document Retrieval
by Sujoy Roychowdhury, Sumit Soman, H. G. Ranjani, Vansh Chhabra, Neeraj Gunda, Shashank Gautam, Subhadip Bandyopadhyay, Sai Krishna Bala
First submitted to arxiv on: 18 Jun 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 Medium Difficulty Summary: This research paper presents a study on sentence embedding models, specifically focusing on domain adaptation using telecom, health, and science datasets for question answering tasks. The authors evaluate publicly available models and their domain-adapted variants based on point retrieval accuracies and confidence intervals. They also introduce metrics to measure the distributional overlaps of correct and random document similarities with the question. The results show that fine-tuning improves mean bootstrapped accuracies, while pre-training followed by fine-tuning further improves accuracy and tightens confidence intervals. Additionally, the study finds that the isotropy of embeddings is poorly correlated with retrieval performance and that domain-specific sentences have little overlap with domain-agnostic ones. The authors provide recommendations for using their methodology and metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: This research paper helps solve a problem in computer science by making it easier to choose the right “sentence embedding model” for certain types of questions. They take publicly available models and adapt them to work better with specific kinds of data, like healthcare or scientific information. The researchers test these adapted models to see how well they perform on different tasks, and they come up with new ways to measure how good these models are. Their results show that making the models more specialized for certain types of questions can make them work better. They also find that there is not much overlap between what works well for one type of question and what works well for another. |
Keywords
» Artificial intelligence » Domain adaptation » Embedding » Fine tuning » Question answering
