Table of Contents
This project tries to systematically explore strategies that help generate prompts for LLMs to extract relevant entities from job descriptions and also to classify web pages given only a few examples of human scores.
A client has a system that collects news artifacts from web pages, tweets, facebook posts, etc. The client is interested in scoring a given new artifact against a topic. The client has hired experts to score a few of these news items in the range from 0 to 10; a score of 0 means the news item is totally NOT relevant while a score of 10 means the news item is very relevant. The range of results between 0 and 10 signifies the degree of relevance of the news item to the topic. The client wants to explore how useful existing LLMs such as GPT-3 are for this task. You are hired as a consultant to explore the efficiency of GPT3-like LLMs to this task. If your recommendation is positive, you must demonstrate that your strategies to design prompts are reproducible and produce a consistent result.
The project is divided and implemented by the following phases:
- Design optimal prompts
- Setup MLOps pipeline that helps automate the task of using different LLMs and different topics.
- Apply the optimal desing strategy
- Entity extraction and Relationship extraction
- Score news titles based on relevance.
- API end-point creation.