# ASPIRE: Assistive System for Performance Evaluation in IR

arXiv: https://arxiv.org/abs/2412.15759 · HTML: https://mlsanity.com/paper/2412.15759/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2024.

## Built on by (most cited first, 1 of 1)

- [Mixture of Experts Approaches in Dense Retrieval Tasks](https://mlsanity.com/paper/2510.15683/) (2025)

## Abstract (arXiv)

Information Retrieval (IR) evaluation involves far more complexity than merely presenting performance measures in a table. Researchers often need to compare multiple models across various dimensions, such as the Precision-Recall trade-off and response time, to understand the reasons behind the varying performance of specific queries for different models. We introduce ASPIRE (Assistive System for Performance Evaluation in IR), a visual analytics tool designed to address these complexities by providing an extensive and user-friendly interface for in-depth analysis of IR experiments. ASPIRE supports four key aspects of IR experiment evaluation and analysis: single/multi-experiment comparisons, query-level analysis, query characteristics-performance interplay, and collection-based retrieval analysis. We showcase the functionality of ASPIRE using the TREC Clinical Trials collection. ASPIRE is an open-source toolkit available online: https://github.com/GiorgosPeikos/ASPIRE