# LLM-based Listwise Reranking under the Effect of Positional Bias

arXiv: https://arxiv.org/abs/2604.03642 · HTML: https://mlsanity.com/paper/2604.03642/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2026.

## Built on by (most cited first, 3 of 3)

- [Ranked by Position: Order Sensitivity as an Exploitable Attack Surface in LLM Listwise Recommenders](https://mlsanity.com/paper/2607.24869/) (2026)
- [Equal Ranking Quality, Different Decisions: Measuring and Reducing Order Dependence in LLM Scorers](https://mlsanity.com/paper/2608.26762/) (2026)
- [AMBER: Multi-View Adaptive Budget Allocation for Listwise Vision-Language Reranking](https://mlsanity.com/paper/2610.02831/) (2026)

## Abstract (arXiv)

LLM-based listwise passage reranking has attracted attention for its effectiveness in ranking candidate passages. However, these models suffer from positional bias, where passages positioned towards the end of the input are less likely to be moved to top positions in the ranking. We hypothesize that there are two primary sources of positional bias: (1) architectural bias inherent in LLMs and (2) the imbalanced positioning of relevant documents. To address this, we propose DebiasFirst, a method that integrates positional calibration and position-aware data augmentation during fine-tuning. Positional calibration uses inverse propensity scoring to adjust for positional bias by re-weighting the contributions of different positions in the loss function when training. Position-aware augmentation augments training data to ensure that each passage appears equally across varied positions in the input list. This approach markedly enhances both effectiveness and robustness to the original ranking across diverse first-stage retrievers, reducing the dependence of NDCG@10 performance on the position of relevant documents. DebiasFirst also complements the inference-stage debiasing methods, offering a practical solution for mitigating positional bias in reranking.