Advancing Robustness in Neural Information Retrieval: A Comprehensive Survey and Benchmarking Framework
Practical Solutions and Value:
Recent developments in neural information retrieval (IR) models have significantly improved their effectiveness across various IR tasks. These advancements enable the models to better understand and retrieve relevant information in response to user queries.
However, ensuring the reliability of these models in practical applications requires a focus on their robustness, which has become an increasingly significant area of research.
Robustness is essential for dependable performance in real-world situations, encompassing the model’s ability to operate consistently and resiliently in unexpected scenarios.
Key elements of robustness in information retrieval include guarding against adversarial attacks, managing out-of-distribution (OOD) scenarios, and minimizing performance variance across requests.
Improving the resilience of dense retrieval models (DRMs) and neural ranking models (NRMs) is crucial for the general dependability of the IR system.
The study provides a thorough analysis of the current approaches, databases, and assessment criteria applied to the research of resilient neural information retrieval models, offering useful insights for scholars and practitioners.
The team has introduced the BestIR benchmark, a heterogeneous evaluation benchmark available on GitHub, to evaluate the resilience of neural information retrieval models.
The study has significantly advanced the subject of robust neural information retrieval (IR) and offers a standardized framework for evaluating and contrasting the robustness of various IR models.
If you want to evolve your company with AI, stay competitive, and use the Advancing Robustness in Neural Information Retrieval survey and benchmarking framework to redefine your way of work.
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