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Benchmarking Mamba's Document Ranking Performance in the Era of Transformers


Core Concepts
Mamba models show competitive performance in document ranking tasks compared to transformer-based models but have lower training throughput.
Abstract
Transformer structure's success in NLP, CV, and IR. Mamba model's performance in document ranking tasks. Comparison of Mamba models with transformer-based models. Training efficiency and throughput of Mamba models. Experiment setup, methodology, and results. Background on state space models and Mamba. Training details and hyperparameters. Related works and conclusion.
Stats
Transformer architecture requires O(n2) time complexity in training and O(n) time complexity in inference. Mamba models achieve 5× higher throughput than Transformers of similar sizes. Mamba models have lower training throughput compared to efficient transformer implementations.
Quotes
"Mamba models can achieve competitive performance, often matching or even surpassing transformer-based LMs of similar sizes." "Mamba models have the deficiency of lower training throughput compared to efficient attention implementations."

Deeper Inquiries

Can Mamba models be further optimized to improve training throughput

To improve the training throughput of Mamba models, several optimization strategies can be considered. One approach could involve refining the hardware-aware optimization techniques used in the Selective Scan method proposed by Gu and Dao. By further optimizing the utilization of GPU memory and parallel computing capabilities, the computational efficiency of Mamba models can be enhanced. Additionally, exploring advanced parallelization techniques and optimizing the computation flow to reduce sequential recurrence could help boost training throughput. Implementing more efficient data loading mechanisms and minimizing redundant computations during training can also contribute to improving the overall training speed of Mamba models.

What are the implications of Mamba's lower training throughput on real-world applications

The lower training throughput of Mamba models can have significant implications for real-world applications, especially in scenarios where training efficiency is crucial. In practical settings such as large-scale information retrieval systems or real-time recommendation engines, the training throughput directly impacts the model's deployment speed and operational costs. The slower training process of Mamba models may lead to longer development cycles, delayed model updates, and increased computational expenses. Moreover, in time-sensitive applications where rapid model iteration is essential, the lower training throughput of Mamba models could hinder the agility and responsiveness of the system, potentially affecting its competitiveness in dynamic environments.

How can the success of Mamba models in document ranking tasks be extended to other IR tasks

Extending the success of Mamba models in document ranking tasks to other Information Retrieval (IR) tasks involves leveraging the model's strengths in capturing contextual interactions and relevance between query and document tokens. One approach is to adapt Mamba models to tasks such as passage retrieval, question answering, or ad-hoc document retrieval, where understanding the semantic relationships between different text segments is crucial. By fine-tuning Mamba models on diverse IR datasets and optimizing them for specific task objectives, their effectiveness can be evaluated and potentially enhanced for a broader range of IR applications. Additionally, exploring ensemble techniques that combine Mamba models with other state-of-the-art IR models could further improve performance across various IR tasks by leveraging the complementary strengths of different architectures.
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