Recent advancements in neural information retrieval (IR) models have increased their efficacy across various IR tasks. However, in addition to understanding and retrieving relevant information to user queries, it is crucial for these models to demonstrate resilience in real-world applications. Robustness in this context refers to the model’s ability to operate consistently under unexpected conditions, such as out-of-distribution (OOD) situations, adversarial attacks, and managing performance variance across requests.
Adversarial attacks are attempts to feed false information into the IR system with the intent to manipulate it. For search result integrity, resilient models should counter such attacks. OOD scenarios could involve exposure to data not included in the original training datasets. Resilient models need to adapt effectively to these unfamiliar queries and documents. Performance variance measures a model’s consistency across various queries and should exhibit minimal degradation even under adverse conditions.
In dense retrieval models (DRMs) and neural ranking models (NRMs), crucial components of the neural IR pipeline, improving resilience becomes integral to ensuring the dependability of the overall IR system. These models retrieve relevant documents and rank them according to their relevance to the query.
A recent study conducted a comprehensive analysis of the current approaches, datasets, and evaluation metrics used in the research of resilient neural information retrieval models. It also discussed the challenges and potential opportunities in this field, especially in an era characterized by vast language models. The analysis is intended to equip those involved in improving the resilience of IR systems with valuable insights.
The study also introduced a benchmark called BestIR, designed to evaluate the resilience of neural information retrieval models. This tool is available at https://github.com/Davion-Liu/BestIR.
The research makes significant strides in the field of robust neural information retrieval. It provides an exhaustive overview of existing research on robustness in IR, defining robustness, and categorizing its different types. This systematic approach aids in the future development of robust neural IR systems. The study examines evaluation metrics, datasets, and methods related to robustness facets in IR. It collates existing datasets and presents the BestIR benchmark, described comprehensively in the research. This novel tool offers a standardized framework for evaluating and comparing the robustness of various IR models.
Credit for this research goes to the researchers of the project. The related paper and Github can be accessed for further details. To ensure you do not miss out on similar updates in the future, you can join the researchers’ Telegram Channel, LinkedIn Group, and also their ML SubReddit, which presently has over 46k members. You can also subscribe to their newsletter.
