# MultiConIR: Towards multi-condition Information Retrieval

arXiv: https://arxiv.org/abs/2503.08046 · HTML: https://mlsanity.com/paper/2503.08046/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2025.

## Built on by (most cited first, 9 of 9)

- [Not All Features Deserve Attention: Graph-Guided Dependency Learning for Tabular Data Generation with Language Models](https://mlsanity.com/paper/2507.18504/) (2025)
- [Rethinking Reasoning in Document Ranking: Why Chain-of-Thought Falls Short](https://mlsanity.com/paper/2510.08985/) (2025)
- [Attributes as Textual Genes: Leveraging LLMs as Genetic Algorithm Simulators for Conditional Synthetic Data Generation](https://mlsanity.com/paper/2509.02040/) (2025)
- [When Is Enough Not Enough? Illusory Completion in Search Agents](https://mlsanity.com/paper/2602.07549/) (2026)
- [Model-Agnostic Meta Learning for Class Imbalance Adaptation](https://mlsanity.com/paper/2604.18759/) (2026)
- [CompRank: Efficient LLM Reranking via Token-Level Compression and Decoding-Free Scoring](https://mlsanity.com/paper/2606.11700/) (2026)
- [SynH-Rank: Quality-Aware Code Search via Diverse Data Synthesis and Hierarchical Ranking Training](https://mlsanity.com/paper/2607.17139/) (2026)
- [Do Current Retrievers Cover All the Evidence? A Controlled Study of Conjunctive Cross-Page Retrieval](https://mlsanity.com/paper/2607.24165/) (2026)
- [Ovis-Embedding: Pushing the Frontiers of Universal Omni-Modal Embeddings](https://mlsanity.com/paper/2609.25165/) (2026)

## Abstract (arXiv)

Multi-condition information retrieval (IR) presents a significant, yet underexplored challenge for existing systems. This paper introduces MultiConIR, a benchmark specifically designed to evaluate retrieval and reranking models under nuanced multi-condition query scenarios across five diverse domains. We systematically assess model capabilities through three critical tasks: complexity robustness, relevance monotonicity, and query format sensitivity. Our extensive experiments on 15 models reveal a critical vulnerability: most retrievers and rerankers exhibit severe performance degradation as query complexity increases. Key deficiencies include widespread failure to maintain relevance monotonicity, and high sensitivity to query style and condition placement. The superior performance of GPT-4o reveals the performance gap between IR systems and advanced LLM for handling sophisticated natural language queries. Furthermore, this work delves into the factors contributing to reranker performance deterioration and examines how condition positioning within queries affects similarity assessment, providing crucial insights for advancing IR systems towards complex search scenarios. The code and datasets are available at https://github.com/EIT-NLP/MultiConIR