Very Large Language Model as a Unified Methodology of Text Mining
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Abstract (arXiv)
Text data mining is the process of deriving essential information from language text. Typical text mining tasks include text categorization, text clustering, topic modeling, information extraction, and text summarization. Various data sets are collected and various algorithms are designed for the different types of tasks. In this paper, I present a blue sky idea that very large language model (VLLM) will become an effective unified methodology of text mining. I discuss at least three advantages of this new methodology against conventional methods. Finally I discuss the challenges in the design and development of VLLM techniques for text mining.
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References · 7
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2016Neural Architectures for Named Entity Recognitioncited by 550
- 2022Generate rather than Retrieve: Large Language Models are Strong Context Generatorscited by 298
- 2020A Survey of Knowledge-Enhanced Text Generationcited by 120
- 2022Diversifying Content Generation for Commonsense Reasoning with Mixture of Knowledge Graph Expertscited by 19
- 2022Retrieval Augmentation for Commonsense Reasoning: A Unified Approachcited by 14
- 2022A Unified Encoder-Decoder Framework with Entity Memorycited by 7
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