Leveraging Passage Embeddings for Efficient Listwise Reranking with Large Language Models

🌈 Abstract

The article discusses a novel approach called PE-Rank for efficient listwise passage reranking using large language models (LLMs). The key ideas are:

• Leveraging passage embeddings as a good context compression for LLMs, reducing the input length and improving efficiency.
• Introducing a "Dynamic-Constrained Decoding" strategy to dynamically change the decoding space during inference, further accelerating the ranking process.
• Proposing a two-stage training method to align the passage embedding space with the LLM's token embedding space, and then fine-tune the model for ranking tasks using listwise learning to rank loss.

Evaluation results on multiple benchmarks demonstrate that PE-Rank significantly improves efficiency in both prefilling and decoding, while maintaining competitive ranking effectiveness compared to uncompressed methods.

🙋 Q&A

[01] Methodology

1. What are the key components of the PE-Rank approach?

• Using passage embeddings as a compressed representation to replace the original passage text as input to the LLM
• Introducing a "Dynamic-Constrained Decoding" strategy to dynamically change the decoding space during inference
• Employing a two-stage training process: first aligning the passage embedding space with the LLM's token embedding space, then fine-tuning the model for ranking tasks using listwise learning to rank loss

2. How does PE-Rank address the efficiency limitations of previous listwise LLM-based reranking approaches?

• The use of passage embeddings reduces the input length, improving efficiency in both prefilling and decoding stages.
• The "Dynamic-Constrained Decoding" strategy further accelerates the decoding process by constraining the output to only the relevant passage tokens.

3. What are the two stages of the training process for PE-Rank?

1. Alignment stage: Trains a mapping function (MLP) to align the passage embedding space with the LLM's token embedding space, using a text reconstruction task.
2. Learning-to-rank stage: Fine-tunes both the MLP and the LLM using listwise learning to rank loss, leveraging the passage embeddings for ranking.

[02] Experiments

1. What datasets were used to evaluate PE-Rank? The evaluation was conducted on the TREC DL and BEIR benchmarks, which cover a range of retrieval tasks and datasets.

2. How did the efficiency of PE-Rank compare to the baselines? PE-Rank showed significant efficiency advantages over the baselines, reducing the number of consumed tokens and latency by a large margin, while maintaining competitive ranking effectiveness.

3. How did PE-Rank perform compared to the uncompressed listwise reranking methods? PE-Rank achieved comparable or even better ranking performance than the uncompressed listwise reranking methods, demonstrating the effectiveness of its compression approach.

4. How did PE-Rank perform when using different passage embedding models? The results showed that PE-Rank can generalize to different embedding models, though the choice of embedding model can impact the final ranking performance.

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