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arXiv · 2410.10127

MAIR: A Massive Benchmark for Evaluating Instructed Retrieval

Weiwei Sun·Zhengliang Shi·Jiulong Wu·Lingyong Yan·Xinyu Ma·Yiding Liu·Min Cao·Dawei Yin·Zhaochun Ren
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Abstract

Recent information retrieval (IR) models are pre-trained and instruction-tuned on massive datasets and tasks, enabling them to perform well on a wide range of tasks and potentially generalize to unseen tasks with instructions. However, existing IR benchmarks focus on a limited scope of tasks, making them insufficient for evaluating the latest IR models. In this paper, we propose MAIR (Massive Instructed Retrieval Benchmark), a heterogeneous IR benchmark that includes 126 distinct IR tasks across 6 domains, collected from existing datasets. We benchmark state-of-the-art instruction-tuned text embedding models and re-ranking models. Our experiments reveal that instruction-tuned models generally achieve superior performance compared to non-instruction-tuned models on MAIR. Additionally, our results suggest that current instruction-tuned text embedding models and re-ranking models still lack effectiveness in specific long-tail tasks. MAIR is publicly available at https://github.com/sunnweiwei/Mair.

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Weiwei Sun, Zhengliang Shi, Jiulong Wu, Lingyong Yan, Xinyu Ma, Yiding Liu, Min Cao, Dawei Yin, Zhaochun Ren. 2024-10-14. MAIR: A Massive Benchmark for Evaluating Instructed Retrieval. https://arxiv.org/abs/2410.10127

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