A Reality check of the benefits of LLM in business

Large language models (LLMs) have achieved remarkable performance in language understanding and generation tasks by leveraging vast amounts of online texts. Unlike conventional models, LLMs can adapt to new domains through prompt engineering without the need for retraining, making them suitable for various business functions, such as strategic planning, project implementation, and data-driven decision-making. However, their limitations in terms of bias, contextual understanding, and sensitivity to prompts raise concerns about their readiness for real-world applications. This paper thoroughly examines the usefulness and readiness of LLMs for business processes. The limitations and capacities of LLMs are evaluated through experiments conducted on four accessible LLMs using real-world data. The findings have significant implications for organizations seeking to leverage generative AI and provide valuable insights into future research directions. To the best of our knowledge, this represents the first quantified study of LLMs applied to core business operations and challenges.

FedDMF: Privacy-Preserving User Attribute Prediction using Deep Matrix Factorization

User attribute prediction is a crucial task in various industries. However, sharing user data across different organizations faces challenges due to privacy concerns and legal requirements regarding personally identifiable information. Regulations such as the General Data Protection Regulation (GDPR) in the European Union and the Personal Information Protection Law of the People's Republic of China impose restrictions on data sharing. To address the need for utilizing features from multiple clients while adhering to legal requirements, federated learning algorithms have been proposed. These algorithms aim to predict user attributes without directly sharing the data. However, existing approaches typically rely on matching users across companies, which can result in dishonest partners discovering user lists or the inability to utilize all available features. In this paper, we propose a novel algorithm for predicting user attributes without requiring user matching. Our approach involves training deep matrix factorization models on different clients and sharing only the item vectors. This allows us to predict user attributes without sharing the user vectors themselves. The algorithm is evaluated using the publicly available MovieLens dataset and demonstrate that it achieves similar performance to the FedAvg algorithm, reaching 96% of a single model's accuracy. The proposed algorithm is particularly well-suited for improving customer targeting and enhancing the overall customer experience. This paper presents a valuable contribution to the field of user attribute prediction by offering a novel algorithm that addresses some of the most pressing privacy concerns in this area.

Face Clustering for Connection Discovery from Event Images

Social graphs are very useful for many applications, such as recommendations and community detections. However, they are only accessible to big social network operators due to both data availability and privacy concerns. Event images also capture the interactions among the participants, from which social connections can be discovered to form a social graph. Unlike online social graphs, social connections carried by event images can be extracted without user inputs, and hence many social graph-based applications become possible, even without access to online social graphs. This paper proposes a system to discover social connections from event images. By utilizing the social information from even images, such as co-occurrence, a face clustering method is proposed and implemented, and connections can be discovered without the identity of the event participants. By collecting over 40000 faces from over 3000 participants, it is shown that the faces can be well clustered with 80% in F1 score, and social graphs can be constructed. Utilizing offline event images may create a long-term impact on social network analytics.