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Found 3 papers, 1 papers with code
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.
Scenario-Adaptive and Self-Supervised Model for Multi-Scenario Personalized Recommendation
we summarize three practical challenges which are not well solved for multi-scenario modeling: (1) Lacking of fine-grained and decoupled information transfer controls among multiple scenarios.
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