Abstract: Information Retrieval (IR) systems are increasingly deployed across diverse domains and languages, from web search and digital libraries to multilingual question answering and enterprise knowledge management. Yet, the benchmarks used to evaluate these systems often remain narrow in scope, limiting progress and failing to capture the complexity of real-world applications.
In this talk, Dr. Rajiv Ratn Shah and Nandan Thakur will discuss the importance of heterogeneous benchmarking as a foundation for meaningful advances in IR research. They will highlight how broad, diverse, and multilingual benchmarks can better reflect the challenges faced by modern IR systems, ensuring that models are robust, fair, and generalizable.
Key themes will include:
Limitations of current IR benchmarks and their impact on research progress.
The need for evaluation frameworks that span multiple domains, languages, and modalities.
Approaches to designing heterogeneous benchmarks that balance scalability with representativeness.
Case studies demonstrating how richer benchmarking leads to more reliable and impactful IR systems.
The presentation will emphasize that benchmarking is not merely a technical exercise but a critical enabler of innovation. By adopting heterogeneous evaluation strategies, the IR community can accelerate progress toward systems that truly meet the needs of diverse users worldwide.