The Distribution of Etiology and Clinical Presentations of Birth Defects: A Preliminary Assessment of China Birth Cohort Study

Liu, Xiaohang; Liu, Ruixia; Wang, Chen; Yan, Ran; Gao, Shen; Su, Shaofei; Nie, Xiaolu; Su, Jian; Zhang, Enjie; Xie, Shuanghua; Liu, Jianhui; Zhang, Yue; Yue, Wentao; Peng, Xiaoxia; Yin, Chenghong

doi:10.2139/ssrn.4350575

Cited by 1 publication

(1 citation statement)

References 76 publications

(61 reference statements)

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“…Astonished by the miracle exhibited by LLMs, there has sparked a trend in contemplating how to utilize the power of LLMs in the field of recommendation system [8,23,28,42,46,52,59]. Current research on LRS follows a general pipeline: translating recommendation data into natural language input and then utilizing LLMs to generate recommendation results in a natural language form [15,26,29,56,57]. However, due to limitations such as the lack of recommendation data during the pre-training phase of LLMs, directly using LLMs for recommendation can only achieve suboptimal performance, making it necessary to tune LLMs on the recommendation data to unleash their recommendation capabilities [1,2,54].…”

Section: Llm-based Recommendation Systemmentioning

confidence: 99%

Item-side Fairness of Large Language Model-based Recommendation System

Jiang,

Bao,

Zhang

et al. 2024

Proceedings of the ACM on Web Conference 2024

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Recommendation systems for Web content distribution intricately connect to the information access and exposure opportunities for vulnerable populations. The emergence of Large Language Modelsbased Recommendation System (LRS) may introduce additional societal challenges to recommendation systems due to the inherent biases in Large Language Models (LLMs). From the perspective of item-side fairness, there remains a lack of comprehensive investigation into the item-side fairness of LRS given the unique characteristics of LRS compared to conventional recommendation systems. To bridge this gap, this study examines the property of LRS with respect to item-side fairness and reveals the influencing factors of both historical users' interactions and inherent semantic biases of LLMs, shedding light on the need to extend conventional item-side fairness methods for LRS. Towards this goal, we develop a concise and effective framework called IFairLRS to enhance the item-side fairness of an LRS. IFairLRS covers the main stages of building an LRS with specifically adapted strategies to calibrate the recommendations of LRS. We utilize IFairLRS to fine-tune LLaMA, a representative LLM, on MovieLens and Steam datasets, and observe significant item-side fairness improvements. The code can be found in https://github.com/JiangM-C/IFairLRS.git. CCS CONCEPTS• Information systems → Recommender systems.

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