Unveiling Deeper Petrochemical Insights: Navigating Contextual Question Answering with the Power of Semantic Search and LLM Fine-Tuning
Krishi Pallab Saikia, Debdeep Mukherjee, Saranik Mahapatra, Prasun Nandy, Rik Kamal Kumar Das · 2023
In the dynamic and complex landscape of the petrochemical industry, competitiveness and efficiency are paramount. Extracting actionable insights from diverse data sources, such as video transcripts, business process data, and organizational information, has become increasingly vital with technological advancements. Video transcripts hold untapped potential, offering opportunities to enhance products and services. This study introduces a comprehensive approach by leveraging semantic search to retrieve targeted information from video transcripts, business process data, and organizational insights. Semantic search employs natural language understanding and vectorization techniques to extract contextual information, yielding precise results and eliminating the need for manual data sifting. Comparative analysis demonstrates the superiority of semantic search over fine-tuned Large Language Models (LLMs) in relevance and response time. Semantic search consistently produces contextually relevant answers, owing to its vector-based semantic comprehension. Additionally, the response time of semantic search outperforms LLMs, enhancing user satisfaction. This research proposes the first of its kind semantic search-based approach for information retrieval in the petrochemical sector using a fusion of video transcript data with other data sources. By developing a domain-specific question answering system, the paper aims to enhance efficiency, accuracy, and decision-making. The integration of video transcripts, semantic search, and organizational data promises transformative implications for the petrochemical sector. This innovative approach holds the promise of streamlined operations, improved safety, and timely insights. The research contributes to bridging the gap in semantic search and bot-based Question Answer (QA) systems by integrating video transcript data with organizational and process data, enhancing information retrieval within the petrochemical sector.