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cs.AI2026
Safety as a Constraint: Fine-Tuning a LLM Recommender to Explain Itself
Jiashu He, Emma Yanyang Kong, JJ Tan +1
Traditional recommender systems are typically trained to predict what item users will interact with next, but not why. However, offering personalized evidence for why a user might…
cs.AI2026
The Lifecycle of LLM-as-a-Judge for Large-Scale Recommendation Explanations
Emma Yanyang Kong, JJ Tan, Ishan Gupta +8
LLM-as-a-Judge, which leverages a large language model to evaluate natural language generated by another AI application or model, has become a standard, scalable approach for accel…