Catalyzing Multilingual NLP: New Methods for Low-Resource Language Support

This paper talks about a new way to make queries for Large Language Models (LLMs) better. We introduce a method called Query Semantic Complexity (QSC). This method helps to understand how complicated user queries are. It is better than older ways of measuring complexity. Next, we created an algorithm called Adaptive Semantic Query Optimization (ASQO). This algorithm changes how queries are processed based on their complexity. Our method uses a special function that looks at two things: how accurate the answers are and how fast they are given. We did many tests with different LLMs, like GPT-3, T5-11B, and BERT-large. The results showed that our method improves how fast queries are processed, how accurate the answers are, and how happy users feel. We also looked at examples in areas like scientific papers and technical documents to show how our method works in real life. Our method is especially good for complicated queries and works well even when the models are bigger. This gives a good way to make query performance better in LLMs.