Concretized Proposition Prompting Resolves Composition-Knowledge Dichotomy in Large Language Models

LLMs often struggle to balance compositionality with knowledgeability, a challenge we define as Composition-Knowledge Dichotomy. To address this, we propose Concretized Proposition Prompting (CPP), a framework that explicitly concretizes propositions relevant to questions. The results demonstrate that CPP significantly enhances reasoning performance, particularly in medical benchmarks where precise knowledge is paramount, while being competitive on math benchmarks where deductive reasoning is prioritized. Additional experiments reveal that CPP is scalable to various foundation models and parameter sizes, being a fundamental paradigm that bridges the gap between composition- and knowledge-based approaches. Consequently, CPP resolves the composition-knowledge dichotomy by providing a solid foundation for logically organized and factually grounded reasoning.

Cross-channel Recommendation for Multi-channel Retail

An increasing number of brick-and-mortar retailers are expanding their channels to the online domain, transforming them into multi-channel retailers. This transition emphasizes the need for cross-channel recommender systems, aiming to enhance revenue across both offline and online channels. Given that each retail channel represents a separate domain with a unique context, this can be regarded as a cross-domain recommendation (CDR). However, the existing studies on CDR did not address the scenarios where both users and items partially overlap across multi-retail channels which we define as "cross-channel retail recommendation (CCRR)". This paper introduces our original work on CCRR using real-world datasets from a multi-channel retail store. Specifically, (1) we present significant challenges in integrating user preferences across both channels. (2) Accordingly, we propose a novel model for CCRR using a channel-wise attention mechanism to capture different user preferences for the same item on each channel. We empirically validate our model's superiority in addressing CCRR over existing models. (3) Finally, we offer implications for future research on CCRR, delving into our experiment results.