  Recent studies indicate that dense retrieval models struggle to perform well
on a wide variety of retrieval tasks that lack dedicated training data, as
different retrieval tasks often entail distinct search intents. To address this
challenge, in this work we leverage instructions to flexibly describe retrieval
intents and introduce I3, a unified retrieval system that performs
Intent-Introspective retrieval across various tasks, conditioned on
Instructions without any task-specific training. I3 innovatively incorporates a
pluggable introspector in a parameter-isolated manner to comprehend specific
retrieval intents by jointly reasoning over the input query and instruction,
and seamlessly integrates the introspected intent into the original retrieval
model for intent-aware retrieval. Furthermore, we propose progressively-pruned
intent learning. It utilizes extensive LLM-generated data to train I3
phase-by-phase, embodying two key designs: progressive structure pruning and
drawback extrapolation-based data refinement. Extensive experiments show that
in the BEIR benchmark, I3 significantly outperforms baseline methods designed
with task-specific retrievers, achieving state-of-the-art zero-shot performance
without any task-specific tuning.
