Title: Elevating Language Models for Information Retrieval through Instruction Tuning

Large Language Models (LLMs) have demonstrated significant success in natural language processing tasks, but their application to Information Retrieval (IR) tasks presents challenges. To address this gap, a novel dataset called INTERS (INstruction Tuning datasEt foR Search) has been developed to enhance the search capabilities of LLMs. This dataset focuses on key aspects of search-related tasks, including query understanding, document understanding, and the relationship between queries and documents. The concept of Instruction Tuning involves fine-tuning pre-trained LLMs on formatted instances represented in natural language.

The construction of INTERS involved meticulous manual crafting of task descriptions and templates to fit data samples. The dataset has been used to evaluate the performance of LLMs on various search-related tasks, including in-domain and generalizability evaluation. The research also delves into the impact of different settings within INTERS, including the importance of task comprehension, the effectiveness of few-shot learning, and the impact of training data volume on model performance.

This work aims to encourage further research in the area of LLMs, particularly in their application to IR tasks, with the goal of ongoing optimization of instruction-based methods to enhance model performance. Check out the Paper and Github for more information about the project.

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