Shallow Transformer Models Outperform Larger Models for Low-Latency Information Retrieval

Core Concepts

Shallow transformer-based cross-encoder models can outperform larger full-scale models in low-latency retrieval scenarios by scoring more candidate documents within the given latency constraints.

Abstract

The paper investigates the use of shallow transformer-based cross-encoder models for low-latency information retrieval. Cross-encoder models, which jointly encode the query and document, typically achieve state-of-the-art effectiveness in text retrieval. However, they are computationally expensive and struggle with high latency, limiting their use in production retrieval systems.
The authors propose using shallow cross-encoder models (with a limited number of transformer layers) as a solution for low-latency retrieval. Shallow models can score more candidate documents within the same latency window compared to full-scale models. The authors show that this ability to score more candidates can lead to higher effectiveness than the more accurate full-scale models when latency is constrained.
The paper makes the following key contributions:

Proposes a simple and replicable training method for shallow cross-encoders based on the generalized Binary Cross-Entropy (gBCE) training scheme, which does not rely on complex knowledge distillation techniques.
Analyzes the efficiency/effectiveness tradeoffs of cross-encoders of different sizes and demonstrates that shallow cross-encoders outperform full-size models under low-latency constraints.
Shows that shallow cross-encoders can be effective even when used without a GPU, making them practical to run without specialized hardware acceleration.

The experiments on TREC Deep Learning datasets show that shallow cross-encoders, when trained with the gBCE scheme, can significantly outperform larger full-scale models in low-latency scenarios. For example, on the TREC-DL2019 dataset, the smallest TinyBERT-gBCE model achieves a 51% higher NDCG@10 compared to the larger MonoBERT-Large model when the latency is limited to 25ms.

Stats

When the latency limit is 25ms per query, MonoBERT-Large (a cross-encoder based on a full-scale BERT model) can only achieve NDCG@10 of 0.431 on TREC DL 2019.
TinyBERT-gBCE (a cross-encoder based on TinyBERT trained with gBCE) reaches NDCG@10 of 0.652 on TREC DL 2019, a +51% gain over MonoBERT-Large.
On TREC-DL2020, with 50ms latency, the difference in NDCG@10 between TinyBERT-gBCE with CPU inference and GPU inference is only 3%.

Quotes

"When the latency limit is 25ms per query, MonoBERT-Large (a cross-encoder based on a full-scale BERT model) is only able to achieve NDCG@10 of 0.431 on TREC DL 2019, while TinyBERT-gBCE (a cross-encoder based on TinyBERT trained with gBCE) reaches NDCG@10 of 0.652, a +51% gain over MonoBERT-Large."
"On TREC-DL2020, with 50ms latency, the difference in NDCG@10 between TinyBERT-gBCE with CPU inference and GPU inference is only 3%."

Key Insights Distilled From

Shallow Cross-Encoders for Low-Latency Retrieval

by Aleksandr V.... at arxiv.org 04-01-2024

https://arxiv.org/pdf/2403.20222.pdf

Deeper Inquiries

What are the potential applications and use cases for these efficient shallow cross-encoder models beyond information retrieval

Shallow cross-encoder models, with their efficiency and effectiveness in low-latency retrieval tasks, have the potential for various applications beyond information retrieval. One key application is in natural language processing tasks such as question-answering systems, chatbots, and sentiment analysis. These models can quickly process and analyze text data, providing accurate responses or sentiment analysis in real-time. Additionally, shallow cross-encoders can be utilized in recommendation systems to enhance personalized recommendations for users based on their preferences and behavior. Another potential application is in content moderation, where these models can quickly assess and filter out inappropriate or harmful content from online platforms. Furthermore, in the healthcare industry, shallow cross-encoders can assist in medical diagnosis by analyzing patient data and providing relevant information to healthcare professionals promptly.

How can the training and inference of shallow cross-encoders be further optimized to improve their efficiency and effectiveness

To further optimize the training and inference of shallow cross-encoders for improved efficiency and effectiveness, several strategies can be implemented. Firstly, optimizing the architecture of the shallow models by experimenting with different configurations of transformer layers, embedding sizes, and attention heads can enhance their performance. Additionally, incorporating techniques like knowledge distillation or transfer learning from larger models can help improve the generalization and effectiveness of shallow cross-encoders. Furthermore, implementing advanced training strategies such as curriculum learning or reinforcement learning can enhance the learning process of these models. For inference optimization, techniques like quantization, pruning, and model distillation can be applied to reduce the computational resources required for inference, making the models more efficient for deployment in real-world applications.

What are the implications of using shallow cross-encoders for environmental sustainability and energy consumption in large-scale search systems

The use of shallow cross-encoders in large-scale search systems can have significant implications for environmental sustainability and energy consumption. By utilizing more efficient and smaller models, the overall energy consumption and carbon footprint of search systems can be reduced. Shallow cross-encoders require fewer computational resources for training and inference compared to full-scale models, leading to lower energy usage and operational costs. This reduction in energy consumption can contribute to a more sustainable approach to information retrieval and search technologies. Additionally, the efficiency of shallow cross-encoders can enable faster processing of search queries, leading to a more responsive user experience while minimizing energy consumption. Overall, the adoption of shallow cross-encoders in search systems can align with efforts towards environmental conservation and sustainability in the technology sector.