Benchmarking Information Retrieval Models on Complex Retrieval Tasks
Authors: name: Julian Killingback, name: Hamed Zamani

Large language models (LLMs) are incredible and versatile tools for
text-based tasks that have enabled countless, previously unimaginable,
applications. Retrieval models, in contrast, have not yet seen such capable
general-purpose models emerge. To achieve this goal, retrieval models must be
able to perform complex retrieval tasks, where queries contain multiple parts,
constraints, or requirements in natural language. These tasks represent a
natural progression from the simple, single-aspect queries that are used in the
vast majority of existing, commonly used evaluation sets. Complex queries
naturally arise as people expect search systems to handle more specific and
often ambitious information requests, as is demonstrated by how people use
LLM-based information systems. Despite the growing desire for retrieval models
to expand their capabilities in complex retrieval tasks, there exist limited
resources to assess the ability of retrieval models on a comprehensive set of
diverse complex tasks. The few resources that do exist feature a limited scope
and often lack realistic settings making it hard to know the true capabilities
of retrieval models on complex real-world retrieval tasks. To address this
shortcoming and spur innovation in next-generation retrieval models, we
construct a diverse and realistic set of complex retrieval tasks and benchmark
a representative set of state-of-the-art retrieval models. Additionally, we
explore the impact of LLM-based query expansion and rewriting on retrieval
quality. Our results show that even the best models struggle to produce
high-quality retrieval results with the highest average nDCG@10 of only 0.346
and R@100 of only 0.587 across all tasks. Although LLM augmentation can help
weaker models, the strongest model has decreased performance across all metrics
with all rewriting techniques.