A Llama-based model trained on serialized user stories unifies item, carousel, and search ranking and outperforms specialist baselines offline while improving some online metrics and reducing latency.
How to index item ids for recommendation foundation models
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LASAR uses two-stage supervised training plus reinforcement learning to ground semantic IDs, align latent reasoning trajectories to CoT hidden states via KL divergence, and adaptively choose reasoning depth, halving average steps while improving quality on three datasets.
This survey organizes generative recommendation into data, model, and task dimensions, identifying five advantages including world knowledge integration and creative generation while noting challenges in benchmarks and efficiency.
VerifAI is an open-source biomedical QA system that decomposes generated answers into claims and verifies them with a fine-tuned NLI engine to reduce hallucinations and provide traceable citations.
LTRR learns to rank a pool of retrievers by their expected contribution to RAG answer correctness and shows that query-dependent selection beats the best single retriever on QA benchmarks.
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A Survey on Generative Recommendation: Data, Model, and Tasks
This survey organizes generative recommendation into data, model, and task dimensions, identifying five advantages including world knowledge integration and creative generation while noting challenges in benchmarks and efficiency.
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LTRR: Learning To Rank Retrievers for LLMs
LTRR learns to rank a pool of retrievers by their expected contribution to RAG answer correctness and shows that query-dependent selection beats the best single retriever on QA benchmarks.