Optimizing Effectiveness and Efficiency Trade-offs in Large Language Model-based Re-Ranking through Ranked List Truncation

Core Concepts

Ranked list truncation (RLT) can improve the trade-offs between effectiveness and efficiency in large language model-based re-ranking by dynamically trimming the retrieved list on a per-query basis.

Abstract

This paper examines the extent to which established findings on RLT for retrieval are generalizable to the "retrieve-then-re-rank" setup, where the goal is to optimize the trade-offs between effectiveness and efficiency in re-ranking.
The key insights are:

Supervised RLT methods do not show a clear advantage over using a fixed re-ranking depth; potential fixed re-ranking depths can closely approximate the effectiveness/efficiency trade-offs achieved by supervised RLT methods.

The choice of retriever has a substantial impact on RLT for re-ranking: with an effective retriever like SPLADE++ or RepLLaMA, a fixed re-ranking depth of 20 can already yield an excellent effectiveness/efficiency trade-off.

Supervised RLT methods tend to fail to predict when not to carry out re-ranking and seem to suffer from a lack of training data.

The authors reproduce a comprehensive set of RLT methods and conduct extensive experiments on 2 datasets, 8 RLT methods, and pipelines involving 3 retrievers and 2 re-rankers. The findings provide a comprehensive understanding of how RLT methods generalize to the new "retrieve-then-re-rank" perspective.

Stats

The average re-ranking cut-off across all test set queries can be used to evaluate re-ranking efficiency.
The nDCG@10 metric is used to evaluate re-ranking effectiveness.

Quotes

None.

Key Insights Distilled From

Ranked List Truncation for Large Language Model-based Re-Ranking

by Chuan Meng,N... at arxiv.org 04-30-2024

https://arxiv.org/pdf/2404.18185.pdf

Deeper Inquiries

How can supervised RLT methods be improved to better predict when not to carry out re-ranking and to overcome the lack of training data?

In order to enhance the performance of supervised RLT methods in predicting when not to carry out re-ranking and to address the issue of limited training data, several strategies can be implemented:

Data Augmentation: One approach to overcome the lack of training data is through data augmentation techniques. By generating synthetic data or augmenting existing data with noise, variations, or perturbations, the supervised RLT models can be exposed to a more diverse set of scenarios, leading to better generalization and improved prediction accuracy.

Transfer Learning: Leveraging pre-trained models or knowledge from related tasks can help in transferring knowledge to the RLT model. By fine-tuning the pre-trained models on the specific RLT task, the model can learn better representations and improve its predictive capabilities, especially in cases where training data is limited.

Ensemble Methods: Combining multiple supervised RLT models into an ensemble can help in capturing diverse patterns and making more robust predictions. By aggregating the predictions of multiple models, the ensemble can provide more reliable and accurate predictions, even in cases where individual models may lack sufficient training data.

Active Learning: Implementing active learning strategies can help in selecting the most informative data points for labeling, thereby maximizing the learning efficiency of the supervised RLT model. By iteratively selecting the most uncertain or informative instances for labeling, the model can learn more effectively from limited training data.

Regularization Techniques: Applying regularization techniques such as dropout, L1/L2 regularization, or early stopping can help prevent overfitting and improve the generalization capabilities of the supervised RLT model. By controlling the model complexity and reducing the risk of memorizing noise in the training data, regularization techniques can enhance the model's ability to predict when not to carry out re-ranking.

By incorporating these strategies, supervised RLT methods can be enhanced to better predict when to carry out re-ranking and mitigate the challenges posed by limited training data.

How can the insights from this study be applied to other information retrieval tasks beyond re-ranking, where the trade-off between effectiveness and efficiency is crucial?

The insights gained from this study on ranked list truncation (RLT) for re-ranking can be extrapolated and applied to various other information retrieval tasks where the trade-off between effectiveness and efficiency is critical. Some ways in which these insights can be leveraged in different IR tasks include:

Query Expansion: In tasks like query expansion, where expanding the initial query with additional terms can improve retrieval effectiveness but may also increase computational costs, the trade-off between effectiveness and efficiency is crucial. RLT methods can be employed to dynamically adjust the length of the expanded query based on the specific requirements of each query, optimizing the trade-off between relevance and computational resources.

Document Summarization: In document summarization tasks, where generating concise summaries while maintaining relevant information is essential, RLT techniques can be utilized to truncate the summary length based on the importance of the content. By dynamically adjusting the summary length, the summarization process can be optimized for both effectiveness and efficiency.

Personalized Search: For personalized search tasks, where tailoring search results to individual user preferences is key, RLT methods can be used to customize the ranking of search results based on user feedback and behavior. By dynamically adjusting the ranking order of search results, personalized search can be optimized to balance relevance and user satisfaction efficiently.

Adaptive Information Retrieval: In adaptive information retrieval scenarios, where the retrieval process needs to adapt to changing user needs and preferences, RLT techniques can be employed to dynamically adjust retrieval strategies based on real-time feedback. By optimizing the trade-off between relevance and responsiveness, adaptive IR systems can deliver more tailored and efficient retrieval results.

By applying the insights from this study to a broader range of information retrieval tasks, practitioners can optimize the trade-off between effectiveness and efficiency in various IR applications, leading to more tailored and efficient retrieval outcomes.