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

Explore the concept of ranked list truncation for large language model-based re-ranking in this 15-minute conference talk presented at SIGIR 2024. Delve into the research conducted by Chuan Meng, Negar Arabzadeh, Arian Askari, Mohammad Aliannejadi, and Maarten de Rijke as they address efficiency challenges in search systems. Gain insights into innovative techniques for improving the performance of large language models in re-ranking tasks, with a focus on optimizing the truncation of ranked lists. Learn about the potential implications of this research for enhancing search efficiency and effectiveness in various applications.

SIGIR 2024 M1.3 [rr] Ranked List Truncation for Large Language Model-based Re-Ranking