RankVicuna: Zero-Shot Listwise Document Reranking with Open-Source Large Language Models (2309.15088v1)

Abstract: Researchers have successfully applied LLMs such as ChatGPT to reranking in an information retrieval context, but to date, such work has mostly been built on proprietary models hidden behind opaque API endpoints. This approach yields experimental results that are not reproducible and non-deterministic, threatening the veracity of outcomes that build on such shaky foundations. To address this significant shortcoming, we present RankVicuna, the first fully open-source LLM capable of performing high-quality listwise reranking in a zero-shot setting. Experimental results on the TREC 2019 and 2020 Deep Learning Tracks show that we can achieve effectiveness comparable to zero-shot reranking with GPT-3.5 with a much smaller 7B parameter model, although our effectiveness remains slightly behind reranking with GPT-4. We hope our work provides the foundation for future research on reranking with modern LLMs. All the code necessary to reproduce our results is available at https://github.com/castorini/rank_LLM.

Rank: Zero-Shot Listwise Document Reranking with Open-Source LLMs

The paper "Rank: Zero-Shot Listwise Document Reranking with Open-Source LLMs" introduces a significant advancement in the field of information retrieval by presenting Rank, an open-source LLM specifically designed for listwise reranking of documents in a zero-shot setting. This work addresses the limitations of relying on proprietary models, which often suffer from issues of non-reproducibility and non-determinism, by leveraging an open-source approach.

Key Contributions

The primary contribution of this paper is the introduction of Rank, the first open-source LLM capable of performing zero-shot listwise reranking. The authors demonstrate that this model achieves competitive effectiveness compared to other systems using proprietary models such as ChatGPT. Remarkably, the Rank model accomplishes this with a significantly smaller 7B parameter model, which aligns with the efficiency goals in AI research.

Experimental Validation:

• The authors validate Rank using datasets from the TREC 2019 and 2020 Deep Learning Tracks.
• Rank achieves comparable effectiveness to reranking with proprietary models like GPT-3.5, though it slightly trails GPT-4.
• The model's performance is highlighted by its strong results, particularly when paired with improved first-stage retrieval methods.

Methodological Innovations

Prompt Design:

• The paper emphasizes novel prompt strategies that leverage the capabilities of models like Vicuna for effective reranking.
• The prompt structure allows the model to capture listwise relevance, attending to multiple documents and their relative positions simultaneously.

Teacher-Student Framework:

• Utilizing a teacher-student model architecture, Rank employs a teacher model (GPT-3.5) to train its Vicuna-based setup, enhancing reranking capabilities without task-specific supervision.

Data Augmentation:

• Shuffling techniques are employed in training to enhance model robustness, allowing Rank to maintain effectiveness despite variations in initial candidate orderings.

Implications and Future Directions

The outcomes of this research highlight several implications for the field of information retrieval:

• Reproducibility: With all code and models made publicly available, Rank ensures reproducibility, enabling other researchers to build upon these findings.
• Model Size and Efficiency: Rank's ability to match or outperform resource-intensive models with fewer parameters underscores the importance of efficient model design.
• Robustness Considerations: The use of data augmentation reflects a growing emphasis on developing models that are robust to irregularities in candidate documents.

Moving forward, the research community may explore optimizing LLMs for diverse reranking tasks by incorporating more complex data augmentation techniques or integrating other retrieval methods. As AI continues to impact various domains, advancements like Rank provide a viable path toward more effective and accessible information retrieval solutions.

Conclusion

The research introduces Rank, establishing a new benchmark for open-source, zero-shot reranking with LLMs. Through a detailed examination of retrieval models and techniques, the paper contributes valuable insights into the development of scalable and efficient information retrieval systems. As the demand for robust retrieval-augmented models grows, Rank positions itself as a foundational element in the evolution of AI-driven retrieval applications.