INTERS: Unlocking the Power of Large Language Models in Search with Instruction Tuning
(2401.06532)Abstract
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 a 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 20 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 IR tasks. Furthermore, we conduct extensive experiments to analyze the effects of instruction design, template diversity, few-shot demonstrations, and the volume of instructions on performance. We make our dataset and the fine-tuned models publicly accessible at~\url{https://github.com/DaoD/INTERS}.
Overview
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The paper introduces INTERS, a novel dataset designed to improve LLMs' performance on information retrieval (IR) tasks through instruction tuning.
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INTERS, which stands for INstruction Tuning datasEt foR Search, includes tasks from 43 unique datasets to help LLMs in query and document understanding.
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Empirical results show that LLMs like LLaMA, Mistral, and Phi significantly benefit from instruction tuning with INTERS, enhancing their search task abilities.
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Researchers conducted experiments to measure the effect of instruction design, training data volume, and task variety, highlighting the importance of detailed tasks and instructional data diversity.
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The INTERS dataset and resulting models are publicly accessible, encouraging transparency in research and offering resources for further improvement in the field.
Introduction to INTERS
In the realm of NLP, the integration of LLMs in information retrieval (IR) has brought about some exciting developments. While LLMs have been making strides in various tasks, their performance in IR-specific tasks has been somewhat inconsistent, especially when compared to smaller models. The discrepancy is attributed to the complexity of IR-specific concepts and their rarity in natural language data, which makes them difficult for LLMs to comprehend. However, a new approach known as "instruction tuning" is emerging as a solution to overcome these challenges and enhance the IR capabilities of LLMs.
Enhancing Language Models' IR Performance
Addressing the challenges associated with LLMs and IR tasks, a novel dataset called INTERS was created. INTERS stands for INstruction Tuning datasEt foR Search, and as the name suggests, it is designed to refine the search abilities of LLMs. The dataset encompasses 21 tasks, deriving from 43 unique datasets, which help models improve in query understanding, document understanding, and comprehending the relationship between queries and documents. The ultimate goal of INTERS is to provide LLMs with the foundation to be instruction-tuned specifically for search-related tasks, thus unlocking their potential in this domain.
Empirical Results and Dataset Accessibility
The dataset not only establishes a new benchmark for enabling LLMs to perform search tasks more effectively but also offers a stepping stone for the models to excel in tasks they haven't directly learned from. Various publicly accessible LLMs like LLaMA, Mistral, and Phi have shown significant performance boosts when fine-tuned with INTERS. Moreover, to aid in transparency and further research, the novel INTERS dataset, and fine-tuned models are made publicly accessible, providing ample opportunity for replication and further enhancement by the research community.
Deep Dive into Experimentation
By conducting rigorous experiments, the researchers of this work dissected multiple aspects: the impact of different instruction designs, the influence of the volume of training data, and the relevance of task variety in improving LLM performance. They found that detailed task descriptions and a diversity of instructional data are vital in instruction tuning. Interestingly, few-shot examples, where models get a few examples of a task, have proven to aid models in adjusting to new tasks remarkably well. This work encapsulates comprehensive and insightful experimentation that enhances the understanding and optimization of LLMs for IR tasks.
In conclusion, INTERS is a robust and specialized instructional tuning dataset that stands out for its comprehensive design tailored to search tasks. It is not only effective in improving performance across a wide range of search-related tasks but also facilitates a better understanding of the factors influencing model optimization for IR tasks. The release of INTERS and its resulting models promises to be invaluable to researchers and practitioners aiming to push the boundaries of LLM applications in search.
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