# Enhancing the Ranking Context of Dense Retrieval Methods through Reciprocal Nearest Neighbors

arXiv: https://arxiv.org/abs/2305.15720 · HTML: https://mlsanity.com/paper/2305.15720/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2023.

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

- [Hard Negatives, Hard Lessons: Revisiting Training Data Quality for Robust Information Retrieval with LLMs](https://mlsanity.com/paper/2505.16967/) (2025)
- [Backdoor Attacks on Dense Retrieval via Public and Unintentional Triggers](https://mlsanity.com/paper/2402.13532/) (2024)
- [Beyond Contrastive Learning: Synthetic Data Enables List-wise Training with Multiple Levels of Relevance](https://mlsanity.com/paper/2503.23239/) (2025)
- [Trove: A Flexible Toolkit for Dense Retrieval](https://mlsanity.com/paper/2511.01857/) (2025)
- [Learning to Retrieve with Weakened Labels: Robust Training under Label Noise](https://mlsanity.com/paper/2512.13237/) (2025)

## Abstract (arXiv)

Sparse annotation poses persistent challenges to training dense retrieval models; for example, it distorts the training signal when unlabeled relevant documents are used spuriously as negatives in contrastive learning. To alleviate this problem, we introduce evidence-based label smoothing, a novel, computationally efficient method that prevents penalizing the model for assigning high relevance to false negatives. To compute the target relevance distribution over candidate documents within the ranking context of a given query, we assign a non-zero relevance probability to those candidates most similar to the ground truth based on the degree of their similarity to the ground-truth document(s). To estimate relevance we leverage an improved similarity metric based on reciprocal nearest neighbors, which can also be used independently to rerank candidates in post-processing. Through extensive experiments on two large-scale ad hoc text retrieval datasets, we demonstrate that reciprocal nearest neighbors can improve the ranking effectiveness of dense retrieval models, both when used for label smoothing, as well as for reranking. This indicates that by considering relationships between documents and queries beyond simple geometric distance we can effectively enhance the ranking context.