In today's fast-paced world, businesses are constantly looking for ways to streamline their processes and accelerate decision-making. One such area is the evaluation and ranking of complex documents. This presentation will explore how leveraging open-source tools like pdf2txt, GPT summarization, and Azure OpenAI Service can help organizations automate and optimize their document evaluation processes while maintaining anonymity and confidentiality.
We will demonstrate a three-level approach that combines the power of GPT models with human evaluation criteria, allowing organizations to efficiently analyze and rank documents based on their relevance and quality:
- Basic Matching and Ranking: In this level, we will use pdf2txt for document extraction and GPT models to match description criteria with items. The system will then rank the items based on their relevance, providing a basic analysis of the documents.
- Incorporating Human Evaluation Criteria: Building on the basic matching and ranking, we will introduce human evaluation criteria that guide the GPT models in their analysis. This approach allows for a more nuanced and human-like understanding of the documents, enabling more accurate and meaningful ranking.
- GPT Model Fine-tuning with Human-ranked Samples: In the final level, we will provide the GPT models with samples of ranked items evaluated by human experts. The models will learn from these samples and fine-tune their ranking capabilities. Once trained, the GPT models can then be applied to new sets of documents, resulting in a more accurate and efficient ranking process that closely mirrors human judgment.
This presentation will showcase how the combination of open-source technologies and innovative approaches can help organizations accelerate their operations and improve decision-making. Attendees will gain insights into the practical applications of these techniques and learn how to implement them in their own organizations to streamline processes and enhance efficiency.
