LISTT5: Improving Zero-shot Retrieval with Fusion-in-Decoder

Core Concepts

LISTT5, a listwise reranking model based on Fusion-in-Decoder, outperforms existing models in zero-shot retrieval tasks.

Abstract

LISTT5 is a novel reranking approach that handles multiple candidate passages efficiently. It improves efficiency and performance in zero-shot retrieval tasks compared to state-of-the-art models. The model overcomes limitations of previous listwise rerankers and demonstrates robustness to positional bias. By leveraging the Fusion-in-Decoder architecture, LISTT5 provides significant gains in NDCG@10 scores and computational efficiency.

Stats

LISTT5 (1) outperforms RankT5 baseline with +1.3 gain in NDCG@10 score. LISTT5 has an efficiency comparable to pointwise ranking models and surpasses the efficiency of previous listwise ranking models.

Quotes

"LISTT5 provides significant contributions in computational efficiency, robustness to positional bias, and zero-shot performance." "Efficiency improvements are demonstrated through FLOPs analysis, showcasing the advantages of LISTT5 over existing models."

Key Insights Distilled From

ListT5

by Soyoung Yoon... at arxiv.org 02-29-2024

https://arxiv.org/pdf/2402.15838.pdf

Deeper Inquiries

How can LISTT5's efficiency be further optimized for real-time applications

LISTT5's efficiency for real-time applications can be further optimized by implementing early stopping during sequential decoding. By setting a threshold for the number of decoding steps, the model can stop processing once it reaches a certain point in generating the output sequence. This approach could significantly reduce the computational load and speed up inference time, making LISTT5 more suitable for real-time applications.

What counterarguments exist against the effectiveness of Fusion-in-Decoder architecture in improving reranking performance

Counterarguments against the effectiveness of Fusion-in-Decoder (FiD) architecture in improving reranking performance may include concerns about overfitting to specific datasets or tasks. Since FiD modifies the Encoder-Decoder structure of T5 to handle listwise inputs efficiently, there might be limitations in its generalizability across different domains or data distributions. Additionally, some critics may argue that FiD could introduce additional complexity without significant improvements in performance metrics, leading to potential trade-offs between efficiency and effectiveness.

How might LISTT5's innovative approach impact the future development of information retrieval systems

The innovative approach of LISTT5 with Fusion-in-Decoder architecture is poised to have a significant impact on the future development of information retrieval systems. By enabling efficient listwise reranking with multiple candidate passages at both training and inference times, LISTT5 sets a new standard for zero-shot retrieval tasks. Its ability to overcome positional bias issues commonly found in large language models opens up possibilities for more accurate and robust ranking mechanisms. As researchers continue to explore ways to enhance retrieval models' capabilities while maintaining computational efficiency, LISTT5's success could inspire further advancements in this field towards more effective and scalable information retrieval systems.