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Researchers have successfully applied large language models (LLMs) such asChatGPT to reranking in an information retrieval context, but to date, suchwork has mostly been built on proprietary models hidden behind opaque APIendpoints. This approach yields experimental results that are not reproducibleand non-deterministic, threatening the veracity of outcomes that build on suchshaky foundations. To address this significant shortcoming, we presentRankVicuna, the first fully open-source LLM capable of performing high-qualitylistwise reranking in a zero-shot setting. Experimental results on the TREC2019 and 2020 Deep Learning Tracks show that we can achieve effectivenesscomparable to zero-shot reranking with GPT-3.5 with a much smaller 7B parametermodel, although our effectiveness remains slightly behind reranking with GPT-4.We hope our work provides the foundation for future research on reranking withmodern LLMs. All the code necessary to reproduce our results is available athttps://github.com/castorini/rank_llm.

RankVicuna: Zero-Shot Listwise Document Reranking with Open-Source Large Language Models