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arXiv · 2210.13578

Speeding Up Question Answering Task of Language Models via Inverted Index

Xiang Ji·Yesim Sungu-Eryilmaz·Elaheh Momeni·Reza Rawassizadeh
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Abstract

Natural language processing applications, such as conversational agents and their question-answering capabilities, are widely used in the real world. Despite the wide popularity of large language models (LLMs), few real-world conversational agents take advantage of LLMs. Extensive resources consumed by LLMs disable developers from integrating them into end-user applications. In this study, we leverage an inverted indexing mechanism combined with LLMs to improve the efficiency of question-answering models for closed-domain questions. Our experiments show that using the index improves the average response time by 97.44%. In addition, due to the reduced search scope, the average BLEU score improved by 0.23 while using the inverted index.

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Xiang Ji, Yesim Sungu-Eryilmaz, Elaheh Momeni, Reza Rawassizadeh. 2022-10-24. Speeding Up Question Answering Task of Language Models via Inverted Index. https://arxiv.org/abs/2210.13578

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