# Do Large Language Models Favor Recent Content? A Study on Recency Bias in LLM-Based Reranking

arXiv: https://arxiv.org/abs/2509.11353 · HTML: https://mlsanity.com/paper/2509.11353/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2025.

## Built on by (most cited first, 10 of 10)

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- [Asking For An Old Friend: Diagnosing and Mitigating Temporal Failure Modes in LLM-based Statutory Question Answering](https://mlsanity.com/paper/2605.23497/) (2026)
- [Persona Conditioning as an Assessor-Sensitivity Probe for LLM-Based IR Evaluation](https://mlsanity.com/paper/2608.10385/) (2026)
- [An Investigation of Linguistic Biases in LLM-Based Recommendations](https://mlsanity.com/paper/2604.25456/) (2026)
- [On the Role of Citations in Preference Data](https://mlsanity.com/paper/2608.21376/) (2026)
- [RAG-NAROK: Retrieval-Aware Knowledge Corpus Poisoning in RAG with Source-specific Refutation](https://mlsanity.com/paper/2609.25469/) (2026)
- [Where's Waldo? Query-language Preference under Cross-lingual Knowledge Disparities](https://mlsanity.com/paper/2610.00606/) (2026)

## Abstract (arXiv)

Large language models (LLMs) are increasingly deployed in information systems, including being used as second-stage rerankers in information retrieval pipelines, yet their susceptibility to recency bias has received little attention. We investigate whether LLMs implicitly favour newer documents by prepending artificial publication dates to passages in the TREC Deep Learning passage retrieval collections in 2021 (DL21) and 2022 (DL22). Across seven models, GPT-3.5-turbo, GPT-4o, GPT-4, LLaMA-3 8B/70B, and Qwen-2.5 7B/72B, "fresh" passages are consistently promoted, shifting the Top-10's mean publication year forward by up to 4.78 years and moving individual items by as many as 95 ranks in our listwise reranking experiments. Although larger models attenuate the effect, none eliminate it. We also observe that the preference of LLMs between two passages with an identical relevance level can be reversed by up to 25% on average after date injection in our pairwise preference experiments. These findings provide quantitative evidence of a pervasive recency bias in LLMs and highlight the importance of effective bias-mitigation strategies.