Large Language Model Augmented Exercise Retrieval for Personalized Language Learning

arXiv:2402.16877v1 Announce Type: cross
Abstract: We study the problem of zero-shot exercise retrieval in the context of online language learning, to give learners the ability to explicitly request personalized exercises via natural language. Using real-world data collected from language learners, we observe that vector similarity approaches poorly capture the relationship between exercise content and the language that learners use to express what they want to learn. This semantic gap between queries and content dramatically reduces the effectiveness of general-purpose retrieval …

abstract arxiv context cs.ai cs.cl cs.ir cs.lg data exercise language language model large language large language model natural natural language observe personalized relationship retrieval study type vector via world zero-shot