# A Two-Stage Adaptation of Large Language Models for Text Ranking

arXiv: https://arxiv.org/abs/2311.16720 · HTML: https://mlsanity.com/paper/2311.16720/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2023.

## Built on by (most cited first, 18 of 18)

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- [MixLM: High-Throughput and Effective LLM Ranking via Text-Embedding Mix-Interaction](https://mlsanity.com/paper/2512.07846/) (2025)
- [SERM: Self-Evolving Relevance Model with Agent-Driven Learning from Massive Query Streams](https://mlsanity.com/paper/2601.09515/) (2026)
- [Semantic Search At LinkedIn](https://mlsanity.com/paper/2602.07309/) (2026)
- [QBD-RankedDataGen: Generating Custom Ranked Datasets for Improving Query-By-Document Search Using LLM-Reranking with Reduced Human Effort](https://mlsanity.com/paper/2505.04732/) (2025)
- [The Vulnerability of LLM Rankers to Prompt Injection Attacks](https://mlsanity.com/paper/2602.16752/) (2026)
- [One Size Does Not Fit All! Dynamic Retriever and Generator Selection for RAG](https://mlsanity.com/paper/2609.17709/) (2026)

## Abstract (arXiv)

Text ranking is a critical task in information retrieval. Recent advances in pre-trained language models (PLMs), especially large language models (LLMs), present new opportunities for applying them to text ranking. While supervised fine-tuning (SFT) with ranking data has been widely explored to better align PLMs with text ranking goals, previous studies have focused primarily on encoder-only and encoder-decoder PLMs. Research on leveraging decoder-only LLMs for text ranking remains scarce. An exception to this is RankLLaMA, which uses direct SFT to explore LLaMA's potential for text ranking. In this work, we propose a two-stage progressive paradigm to better adapt LLMs to text ranking. First, we conduct continual pre-training (CPT) of LLMs on a large weakly-supervised corpus. Second, we perform SFT, and propose an improved optimization strategy building upon RankLLaMA. Our experimental results on multiple benchmarks show that our approach outperforms previous methods in both in-domain and out-domain scenarios.