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Found 4 papers, 2 papers with code
End-to-end Learnable Clustering for Intent Learning in Recommendation
Concretely, we encode users' behavior sequences and initialize the cluster centers (latent intents) as learnable neurons.
Bridging Code Semantic and LLMs: Semantic Chain-of-Thought Prompting for Code Generation
Meanwhile, while traditional techniques leveraging such semantic information require complex static or dynamic code analysis to obtain features such as data flow and control flow, SeCoT demonstrates that this process can be fully automated via the intrinsic capabilities of LLMs (i. e., in-context learning), while being generalizable and applicable to challenging domains.
At Which Training Stage Does Code Data Help LLMs Reasoning?
Inspired by the great success of code data in training LLMs, we naturally wonder at which training stage introducing code data can really help LLMs reasoning.
Language-Enhanced Session-Based Recommendation with Decoupled Contrastive Learning
Furthermore, we introduce a novel Decoupled Contrastive Learning method to enhance the effectiveness of the language representation.
