# Constructing Set-Compositional and Negated Representations for First-Stage Ranking

arXiv: https://arxiv.org/abs/2501.07679 · HTML: https://mlsanity.com/paper/2501.07679/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jan 2025.

## Built on by (most cited first, 5 of 5)

- [Query Decomposition for RAG: Balancing Exploration-Exploitation](https://mlsanity.com/paper/2510.18633/) (2025)
- [A Comprehensive Taxonomy of Negation for NLP and Neural Retrievers](https://mlsanity.com/paper/2507.22337/) (2025)
- [LogiCoL: Logically-Informed Contrastive Learning for Set-based Dense Retrieval](https://mlsanity.com/paper/2505.19588/) (2025)
- [Tutorial on Reasoning for IR & IR for Reasoning](https://mlsanity.com/paper/2602.03640/) (2026)
- [Reproducing Complex Set-Compositional Information Retrieval](https://mlsanity.com/paper/2605.03824/) (2026)

## Abstract (arXiv)

Set compositional and negated queries are crucial for expressing complex information needs and enable the discovery of niche items like Books about non-European monarchs. Despite the recent advances in LLMs, first-stage ranking remains challenging due to the requirement of encoding documents and queries independently from each other. This limitation calls for constructing compositional query representations that encapsulate logical operations or negations, and can be used to match relevant documents effectively. In the first part of this work, we explore constructing such representations in a zero-shot setting using vector operations between lexically grounded Learned Sparse Retrieval (LSR) representations. Specifically, we introduce Disentangled Negation that penalizes only the negated parts of a query, and a Combined Pseudo-Term approach that enhances LSRs ability to handle intersections. We find that our zero-shot approach is competitive and often outperforms retrievers fine-tuned on compositional data, highlighting certain limitations of LSR and Dense Retrievers. Finally, we address some of these limitations and improve LSRs representation power for negation, by allowing them to attribute negative term scores and effectively penalize documents containing the negated terms.