LLMs and Latent Semantic Indexing: Hidden Connections

Where is Latent Semantic Indexing Used?

It is used for numerous NLP domains, including automated document categories, online customer support, text summaries, and spam filtering. Its most common uses include:

• Search engines – analysing user queries, understand user intent, retrieving relevant pages 
• Document classification – LSI search algorithms can classify documents into predefined categories, such as sentiment, email, and others
• Online customer support – match search queries with relevant solutions in CMS
• Filter spam – LSI can detect and filter spam emails based on semantic content
• Visualisation – document clusters generated by LSI can be plotted to view the relationship between documents.

How LSI and LLM is Connected

Latent semantic indexing (LSI) and LLM are related through the use in understanding and representing text. LSI uses mathematical techniques to capture relationships between words and documents, while LLM leverage the amount of text data to learn contextualised representations of language. Basically LLMs are a sophisticated version of LSI, able to capture the complex semantic relationships.

Goals

• LSI – uncover semantic structure of text, identifying relationships between words and documents
• LLM – trained to understand and generate human language, capturing complex semantic relationships, along with contextual meanings

Technique

• LSI – relies on singular value decomposition (SVD) reducing the dimension of term-document matric, capturing significant semantic dimensions
• LLM – uses deep natural networks, learning vector representations, capturing the semantic relationship between words.

Advantages and Limitations

• LSI – can be expensive for larger datasets and reduced dimensions can be harder to interpret
• LLM – capture nuanced complex semantic relationships, used for translation, question answering, along with generating text

The Hidden Connections

• Semantic representation – both create semantic representations of text, capturing the meaning and relationships between words and concepts
• Information retrieval – improve information retrieval, enabling search engines to understand the semantic meaning behind queries and content, improving the accuracy of search results

Conclusion

LLMs are more sophisticated and powerful that LSI. LLMs are able to capture complex semantic relationships, building upon the foundations that were laid by latent semantic indexing, using sophisticated techniques. LSI provides a foundational approach, while LLMs are more versatile and powerful. If you need any help with your online marketing, contact Genie Crawl today to find out more.