What is it about?
This publication presents a new system that allows companies to quickly find relevant information buried across their many documents, reports, and websites. Companies tend to accumulate vast amounts of text data over time, making it challenging for employees to locate the precise information they need using traditional search methods. The proposed solution utilizes modern language analysis techniques to better understand the meaning and context behind words and queries. This allows it to identify and surface the most pertinent information to a user's search within the company's data repositories. Unlike other advanced AI search approaches, this system does not require massive training datasets or expensive computing resources. As a result, it offers a cost-effective way for organizations to quickly retrieve useful knowledge from their internal information stores without extensive time or money investments. By unlocking easier access to existing data, the system can boost productivity and allow companies to capitalize more fully on the informational resources already available to them.
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Why is it important?
This work is vital for a few key reasons: Firstly, it tackles a widespread challenge that most large organizations face - having too much internal information scattered across various sources, making it difficult for employees to quickly find what they need. As companies accumulate more data over time, this problem worsens without an effective search solution. This approach is unique because it combines the latest language understanding capabilities with a lightweight, cost-efficient architecture. Advanced AI language models can significantly improve search relevance by accounting for context and meaning. However, they usually require immense computational resources. This work shows how to leverage those capabilities through low-cost model fine-tuning. Additionally and more critically, the system maintains transparency in retrieving and ranking information. Unlike black-box AI models that can "hallucinate" outputs, this solution works interpretably based on more apparent language representations and similarity calculations. This transparency can be critical for businesses that must trust and explain their data systems. There is an increasing demand for better organizational knowledge management as remote work becomes more prevalent. With more dispersed digital information access, having an efficient internal search engine is even more crucial now. Overall, this publication offers a novel yet pragmatic way for organizations to modernize their ability to tap into their own information resources in an affordable, transparent, and highly usable manner. This could lead to productivity gains, better data governance, and a competitive business advantage.
Perspectives
We are thrilled to share this work which tackles a fundamental challenge facing most enterprises - finding relevant information scattered across their own internal data repositories. Over years of working with corporate clients, we have witnessed the productivity drains and missed opportunities caused by inefficient information retrieval abilities. This publication represents our efforts to develop a pragmatic, deployable solution. Our critical insight was strategically applying transfer learning from large language models to encode rich semantics computationally efficiently. By fine-tuning BERT on a targeted multi-label classification task specific to an organization's content taxonomy, we could extract highly relevant vector representations and leverage intuitive similarity metrics like Word Movers Distance. This approach balances modern language understanding capabilities with lightweight training requirements and transparent scoring mechanisms. While some transparency limitations remain, this work charts a promising path for embedding powerful AI search into cost-effective, easily governable enterprise knowledge management systems. We are excited to collaborate with business partners to make this a reality and empower companies to fully capitalize on their existing informational assets.
Dr. Sanjay Singh
Manipal Institute of Technology, Manipal
Read the Original
This page is a summary of: Transparent, Low Resource and Context-Aware Information Retrieval from a Closed Domain Knowledge Base, IEEE Access, January 2024, Institute of Electrical & Electronics Engineers (IEEE),
DOI: 10.1109/access.2024.3380006.
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