SF-UBM enables privacy-preserving cross-domain LLM recommendation by federating semantic item representations, distilling domain knowledge, and aligning preferences into LLM soft prompts.
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UNVERDICTED 2representative citing papers
Mask-to-Correct and M2C+ use diversity-aware masking in RAG to identify erroneous claim spans and produce faithful corrections, outperforming baselines by up to 14% SARI without gold evidence.
citing papers explorer
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Federated User Behavior Modeling for Privacy-Preserving LLM Recommendation
SF-UBM enables privacy-preserving cross-domain LLM recommendation by federating semantic item representations, distilling domain knowledge, and aligning preferences into LLM soft prompts.
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Mask-to-Correct$^+$: Leveraging Retriever Diversity for Masking-guided Faithful Fact Correction
Mask-to-Correct and M2C+ use diversity-aware masking in RAG to identify erroneous claim spans and produce faithful corrections, outperforming baselines by up to 14% SARI without gold evidence.