Abstract
The rapid growth of digital information and the increasing complexity of user queries have made traditional search methods less effective in the context of business-related websites. This paper presents an innovative approach to improve the search experience across a variety of domains, particularly in the industrial sector, by integrating semantic search and conversational large language models such as GPT-3.5 into a domain-adaptive question-answering framework. Our proposed solution aims at complementing existing keyword-based approaches with the ability to capture entire questions or problems. By using all types of text, such as product manuals, documentation, advertisements, and other documents, all types of questions relevant to a website can be answered. These questions can be simple requests for product or domain knowledge, assistance in using a product, or more complex questions that may be relevant in determining the value of organizations as potential collaborators. We also introduce a mechanism for users to ask follow-up questions and to establish subject-specific communication with the search system. The results of our feasibility study show that the integration of semantic search and GPT-3.5 leads to significant improvements in the search experience, which could then translate into higher user satisfaction when querying the corporate portfolio. This research contributes to the ongoing development of advanced search technologies and has implications for a variety of industries seeking to unlock their hidden value.
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Notes
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https://solr.apache.org, last accessed 2023-07-24.
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https://openai.com/blog/new-and-improved-embedding-model, last accessed 2023-07-24.
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https://streamlit.io, last accessed 2023-07-24.
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https://haystack.deepset.ai, last accessed 2023-07-24.
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https://platform.openai.com, last accessed 2023-07-24.
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Acknowledgments
This work was co-funded by the German Federal Ministry of Education and Research under grants 13N16242 and 01IO2208E.
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Maoro, F., Vehmeyer, B., Geierhos, M. (2024). Leveraging Semantic Search and LLMs for Domain-Adaptive Information Retrieval. In: Lopata, A., Gudonienė, D., Butkienė, R. (eds) Information and Software Technologies. ICIST 2023. Communications in Computer and Information Science, vol 1979. Springer, Cham. https://doi.org/10.1007/978-3-031-48981-5_12
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