  Information retrieval aims to find information that meets users' needs from
the corpus. Different needs correspond to different IR tasks such as document
retrieval, open-domain question answering, retrieval-based dialogue, etc.,
while they share the same schema to estimate the relationship between texts. It
indicates that a good IR model can generalize to different tasks and domains.
However, previous studies indicate that state-of-the-art neural information
retrieval (NIR) models, e.g, pre-trained language models (PLMs) are hard to
generalize. Mainly because the end-to-end fine-tuning paradigm makes the model
overemphasize task-specific signals and domain biases but loses the ability to
capture generalized essential signals. To address this problem, we propose a
novel NIR training framework named NIR-Prompt for retrieval and reranking
stages based on the idea of decoupling signal capturing and combination.
NIR-Prompt exploits Essential Matching Module (EMM) to capture the essential
matching signals and gets the description of tasks by Matching Description
Module (MDM). The description is used as task-adaptation information to combine
the essential matching signals to adapt to different tasks. Experiments under
in-domain multi-task, out-of-domain multi-task, and new task adaptation
settings show that NIR-Prompt can improve the generalization of PLMs in NIR for
both retrieval and reranking stages compared with baselines.
