Large language models (LLMs) have demonstrated impressive capabilities in
various natural language processing tasks. Despite this, their application to
information retrieval (IR) tasks is still challenging due to the infrequent
occurrence of many IR-specific concepts in natural language. While prompt-based
methods can provide task descriptions to LLMs, they often fall short in
facilitating comprehensive understanding and execution of IR tasks, thereby
limiting LLMs' applicability. To address this gap, in this work, we explore the
potential of instruction tuning to enhance LLMs' proficiency in IR tasks. We
introduce a novel instruction tuning dataset, INTERS, encompassing 21 tasks
across three fundamental IR categories: query understanding, document
understanding, and query-document relationship understanding. The data are
derived from 43 distinct datasets with manually written templates. Our
empirical results reveal that INTERS significantly boosts the performance of
various publicly available LLMs, such as LLaMA, Mistral, and Phi, in
search-related tasks. Furthermore, we conduct a comprehensive analysis to
ascertain the effects of base model selection, instruction design, volume of
instructions, and task variety on performance. We make our dataset and the
models fine-tuned on it publicly accessible at this https URL

本研究探索指令调优提高大型语言模型在信息检索任务中的能力，并引入了一个新的指令调优数据集，INTER，涵盖了查询理解、文档理解和查询 - 文档关系理解三个基本 IR 类别的 21 个任务。实证结果表明，INTER 显著提高了公开可用的 LLMs（如 LLaMA、Mistral 和 Phi）在搜索相关任务中的性能。