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

Speeding Up Question Answering Task of Language Models via Inverted Index