This work introduces the INTERS dataset to enhance the search capabilities of Large Language Models (LLMs) through instruction tuning. The dataset covers various search-related tasks and emphasizes query and document understanding. It demonstrates the effectiveness of instruction tuning in improving LLMs’ performance across different settings and tasks, shedding light on crucial aspects such as few-shot learning and data volumes. For more details, refer to the research paper and corresponding Github repository.
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Enhancing Information Retrieval with Large Language Models Using the INTERS Dataset
Large Language Models (LLMs) have shown great potential in natural language processing tasks, but applying them to Information Retrieval (IR) tasks has been challenging due to the lack of IR-specific concepts in natural language. To address this, instruction tuning has emerged as a key method to enhance LLMs’ capabilities and control.
Introducing the INTERS Dataset
This work introduces the INTERS dataset, meticulously designed to improve the search capabilities of LLMs. The dataset focuses on query understanding, document understanding, and the relationship between queries and documents, covering 20 distinct search-related tasks.
Instruction Tuning and Tasks
Instruction tuning involves fine-tuning pre-trained LLMs on formatted instances represented in natural language. It enhances performance on trained tasks and enables LLMs to generalize to new tasks. The dataset includes tasks such as query understanding, document understanding, and query-document relationship understanding.
Evaluation and Experiments
The study evaluates the effectiveness of instruction tuning on search tasks using various LLMs. It explores the impact of different settings within the dataset and the quantity of training data on model performance.
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