Summary of Leveraging Llms to Enable Natural Language Search on Go-to-market Platforms, by Jesse Yao and Saurav Acharya and Priyaranjan Parida and Srinivas Attipalli and Ali Dasdan
Leveraging LLMs to Enable Natural Language Search on Go-to-market Platforms
by Jesse Yao, Saurav Acharya, Priyaranjan Parida, Srinivas Attipalli, Ali Dasdan
First submitted to arxiv on: 7 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Databases (cs.DB); Information Retrieval (cs.IR); 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 This paper addresses the challenges in enterprise searches, where users struggle to access information due to complex query requirements, configurations, and metadata. The authors identify the limitations of current go-to-market platforms’ advanced search interfaces, which provide too many options, fields, and buttons for users. Instead, they propose using natural language querying, enabled by Large Language Models (LLMs). This approach aims to simplify searching by leveraging the capabilities of LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Enterprise searches are hard because people need to know a lot about what they’re looking for. Most search tools have too many options and buttons, making it hard to find what you need. That’s why this paper thinks we should be able to search using regular language, like we do with humans. This idea is made possible by special AI models called Large Language Models (LLMs). |
