A Comprehensive Comparison of Cross-Encoders and LLMs for Reranking SPLADE

핵심 개념

Cross-encoders and LLMs are compared for reranking SPLADE retrievers, showing competitive performance in different scenarios.

초록

The study compares cross-encoders and LLMs for reranking SPLADE retrievers. Cross-encoders are efficient with strong retrievers, while LLMs show promising results but are costly. In-domain comparisons show subtle differences, while out-of-domain evaluations highlight the effectiveness of DeBERTa-v3 over ELECTRA. Increasing the number of documents to rerank positively impacts effectiveness. GPT-4 performs well but is expensive, while open LLMs have mixed results. Cascading pipelines with LLMs show potential in IR systems.

통계

Reranking 50 documents can take up to 1 minute using GPT-4 on an H100 GPU.
The default document length in RankGPT is |d| = 300.
GPT-4 achieves close results with a smaller k (25 or 50) on TREC-COVID.

인용구

"We notice that strong baselines are often absent or not systematically used in recent works evaluating LLM-based rerankers."
"Overall, increasing the number of documents to re-rank has a positive impact on the final effectiveness."
"GPT-4 exhibits a very surprising ability to re-rank documents."

핵심 통찰 요약

A Thorough Comparison of Cross-Encoders and LLMs for Reranking SPLADE

by Herv... 게시일 arxiv.org 03-18-2024

https://arxiv.org/pdf/2403.10407.pdf

더 깊은 질문

How can cost-effective alternatives be developed for using LLMs in reranking?

To develop cost-effective alternatives for using Large Language Models (LLMs) in reranking, several strategies can be considered:

Model Compression: Utilize techniques like distillation to create smaller, more efficient versions of LLMs that can still maintain a good level of performance. This involves training a smaller model to mimic the behavior of the larger LLM.

Knowledge Distillation: Transfer knowledge from pre-trained LLMs to domain-specific models through distillation, reducing the need for large-scale models during inference.

Quantization and Pruning: Reduce the precision of weights and prune unnecessary connections in LLMs to decrease computational requirements without significant loss in performance.

Hybrid Approaches: Combine traditional methods like cross-encoders with lightweight neural networks or rule-based systems to achieve a balance between efficiency and effectiveness.

Task-Specific Fine-Tuning: Instead of using generic pre-trained LLMs, fine-tune them on specific tasks or datasets relevant to reranking, which may require less computation compared to training from scratch.

By implementing these approaches, it is possible to make the use of LLMs more cost-effective while still benefiting from their powerful capabilities in reranking tasks.

What implications do the findings have for balancing efficiency and effectiveness in information retrieval systems?

The findings suggest that there is a trade-off between efficiency and effectiveness when choosing between different reranking methods in information retrieval systems:

Efficiency vs Effectiveness: Cross-encoder-based rerankers are shown to be competitive against LLM-based ones while being more efficient. This indicates that simpler models can provide comparable results with lower computational costs.

Impact on Retrieval Performance: The study highlights how increasing the number of documents re-ranked has a positive impact on overall effectiveness but comes at an increased computational expense due to resource-intensive models like GPT-4.

Cost Considerations: Balancing efficiency and effectiveness involves considering not just performance metrics but also resource utilization costs associated with deploying complex models like OpenAI's GPT series.

Cascading Pipelines: Using cascading pipelines with both cross-encoders and LLMs allows for leveraging each model's strengths efficiently by selecting candidates effectively before applying computationally expensive re-ranking steps.

In conclusion, understanding this balance is crucial for designing information retrieval systems that deliver optimal performance within resource constraints.

How might the use of cascading pipelines with LLMs impact the future of information retrieval research?

The use of cascading pipelines incorporating Large Language Models (LLMs) could have several impacts on future information retrieval research:

Enhanced Relevance Ranking: Cascading pipelines allow researchers to combine multiple stages of ranking algorithms where each stage refines document rankings further based on relevance signals extracted by different types of models such as cross-encoders followed by advanced language models like GPT-4.

Improved Search Quality: By leveraging complementary strengths across various stages within a pipeline, search quality could significantly improve as each model contributes its unique abilities towards better document ranking accuracy.

3 .Resource Optimization: Cascading enables optimization by selectively applying computationally intensive processes only when necessary—such as employing high-cost operations like running an extensive language model only after initial filtering through lighter-weight algorithms.
4 .Domain Adaptability: Cascaded architectures offer flexibility for adapting system components based on specific domains or data characteristics—allowing tailoring at each step according to varying needs without compromising overall system efficacy.
5 .**Research Advancements: The exploration into cascaded structures involving diverse classes of algorithms will likely drive innovation towards hybrid solutions combining traditional IR methodologies with cutting-edge deep learning techniques - paving new avenues for advancing state-of-the-art practices in Information Retrieval research.