Summary of Automl-guided Fusion Of Entity and Llm-based Representations For Document Classification, by Boshko Koloski et al.
AutoML-guided Fusion of Entity and LLM-based Representations for Document Classification
by Boshko Koloski, Senja Pollak, Roberto Navigli, Blaž Škrlj
First submitted to arxiv on: 19 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 This paper demonstrates the efficacy of injecting semantic knowledge from large text databases into contemporary Large Language Model (LLM)-based representations for text classification tasks. The authors show that this fusion can improve performance, even when using low-dimensional projections obtained via efficient matrix factorization. This approach enables the development of faster classifiers with minimal loss in predictive performance, as demonstrated on six diverse real-life datasets. The authors achieve this by fusing LLM-based representations with embedded information from knowledge bases and utilizing automated machine learning (AutoML) to optimize classification accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper finds a way to make computers better at understanding text by combining two different approaches: large language models and knowledge databases. This combination helps the computer learn to classify text more accurately, even when it’s using a simplified version of the original representation. The authors test their approach on many real-life datasets and show that it can be much faster while still being just as good at making predictions. |
Keywords
» Artificial intelligence » Classification » Large language model » Machine learning » Text classification
