ResRank: Unifying Retrieval and Listwise Reranking via End-to-End Joint Training with Residual Passage Compression
Recent advancements in large language models (LLMs) have significantly transformed the landscape of information retrieval, particularly through the adoption of listwise reranking techniques. These methods have become the benchmark for achieving high ranking effectiveness. However, the typical reliance on complete passage texts presents notable challenges that hinder practical applications in industrial settings. The introduction of ResRank aims to address these challenges head-on.
Challenges in Current Approaches
The existing LLM-based listwise reranking approaches face two primary bottlenecks:
- “Lost in the middle” phenomenon: As the length of input texts increases, the quality of ranking tends to degrade. This issue arises because longer passages can obscure relevant information, leading to ineffective retrieval outcomes.
- Inference latency: The time taken for inference scales super-linearly with the length of the input sequence. This results in inefficiencies that can render LLMs impractical for real-time applications, especially in high-demand industrial scenarios.
Introducing ResRank
In response to these challenges, ResRank is proposed as a unified retrieval-reranking framework. It employs an innovative approach that fundamentally reshapes how passages are processed and ranked:
- Residual Passage Compression: ResRank utilizes an Encoder-LLM to convert each candidate passage into a compact embedding. This transformation significantly reduces the amount of information processed at once, allowing for more efficient handling of inputs.
- Joint Training Framework: The framework integrates a Reranker-LLM that receives the compressed embeddings along with the query text. This synergy between the encoder and reranker is enhanced by a residual connection structure, which mitigates the misalignment between the compressed representation space and the ranking space.
- Cosine-Similarity-Based Scoring: Traditional autoregressive decoding methods are replaced with a one-step scoring mechanism based on cosine similarity. This shift not only simplifies the ranking process but also eliminates the generation bottleneck that has plagued previous models.
Training Efficiency and Effectiveness
The training of ResRank follows a dual-stage, multi-task, end-to-end joint optimization strategy. This comprehensive approach allows for simultaneous training of both the encoder and reranker, ensuring that the learning objectives for retrieval and reranking are aligned effectively. The result is a significant reduction in training complexity while enhancing overall performance.
Performance Evaluation
Extensive experiments conducted on TREC Deep Learning and eight BEIR benchmark datasets validate the efficacy of ResRank. The findings reveal that:
- ResRank achieves competitive or superior ranking effectiveness compared to existing methodologies.
- The model operates with zero generated tokens, processing only one token per passage, which drastically improves efficiency.
- This balance between effectiveness and efficiency makes ResRank a promising solution for real-time industrial applications.
Conclusion
ResRank represents a significant advancement in the realm of information retrieval and reranking. By addressing the limitations of traditional LLM-based approaches, it offers a more efficient and effective framework that is poised to enhance the speed and quality of information retrieval systems across various industries.
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