Leveraging Passage Embeddings for Efficient Listwise Reranking with Large Language Models
๐ 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.
๐ 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?
- 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.
- 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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