Summary of Leveraging Large Language Models For Efficient Representation Learning For Entity Resolution, by Xiaowei Xu et al.
Leveraging large language models for efficient representation learning for entity resolution
by Xiaowei Xu, Bi T. Foua, Xingqiao Wang, Vivek Gunasekaran, John R. Talburt
First submitted to arxiv on: 15 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposes TriBERTa, a supervised entity resolution system that leverages a pre-trained large language model and a triplet loss function to learn representations for entity matching. The system consists of two steps: first, name entity records are fed into a Sentence Bidirectional Encoder Representations from Transformers (SBERT) model to generate vector representations, which are then fine-tuned using contrastive learning based on a triplet loss function. Fine-tuned representations are used as input for entity matching tasks, and the results show that the proposed approach outperforms state-of-the-art representations, including SBERT without fine-tuning and conventional Term Frequency-Inverse Document Frequency (TF-IDF), by a margin of 3-19%. Additionally, the representations generated by TriBERTa demonstrated increased robustness, maintaining consistently higher performance across a range of datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TriBERTa is a new way to match names in data. It uses a big language model and a special loss function to learn how to match names. The system has two steps: first, it puts name records into a special kind of computer program called SBERT, which makes vector representations. Then, it fine-tunes those representations using contrastive learning based on the triplet loss function. TriBERTa is better than other ways of matching names because it gets 3-19% better results. It’s also more robust and works well across different datasets. |
Keywords
» Artificial intelligence » Encoder » Fine tuning » Language model » Large language model » Loss function » Supervised » Tf idf » Triplet loss
