Bibliographic Information: Yan, Y., Wang, Y., Zhang, C., Hou, W., Pan, K., Ren, X., Wu, Z., Zhai, Z., Yu, E., Ou, W., & Song, Y. (2018). LLM4PR: Improving Post-Ranking in Search Engine with Large Language Models. In Proceedings of Make sure to enter the correct conference title from your rights confirmation emai (Conference acronym ’XX). ACM, New York, NY, USA, 10 pages. https://doi.org/XXXXXXX.XXXXXXX
Research Objective: This paper introduces LLM4PR, a novel framework designed to enhance the post-ranking stage in search engines by leveraging the capabilities of large language models (LLMs).
Methodology: LLM4PR addresses the challenges of incorporating heterogeneous features and adapting LLMs for post-ranking tasks. It employs a Query-Instructed Adapter (QIA) to integrate diverse user/item features and a feature adaptation step to align these representations with the LLM. Additionally, it introduces a learning to post-rank step with a main task for generating post-ranking order and an auxiliary task for pairwise list quality comparison.
Key Findings: Experimental results demonstrate that LLM4PR significantly outperforms state-of-the-art methods in post-ranking tasks on both information retrieval and search datasets, including MovieLens-1M and KuaiSAR. Ablation studies highlight the importance of each component in LLM4PR, including QIA, feature adaptation, and the auxiliary task.
Main Conclusions: LLM4PR effectively leverages LLMs for search engine post-ranking, leading to substantial improvements in ranking quality and user satisfaction. The proposed framework offers a promising approach to optimize search results by considering both item relevance and user preferences.
Significance: This research significantly contributes to the field of information retrieval by introducing a novel LLM-based framework for post-ranking, addressing the limitations of traditional methods and paving the way for future research in LLM-powered search engines.
Limitations and Future Research: While LLM4PR demonstrates promising results, future research could explore incorporating user interaction data and investigating the impact of different LLM architectures and pre-training objectives on post-ranking performance.
To Another Language
from source content
arxiv.org
Key Insights Distilled From
by Yang Yan, Yi... at arxiv.org 11-05-2024
https://arxiv.org/pdf/2411.01178.pdfDeeper Inquiries