Generative retrieval beats dense retrieval and BM25 on the LIMIT dataset but degrades with hard negatives due to identifier ambiguity during decoding.
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cs.IR 3years
2026 3representative citing papers
SA²CRQ uses sequential adaptive residual quantization based on path entropy plus anchored curriculum regularization from head items to improve both efficiency and cold-start performance in generative retrieval.
CQ-SID semantic IDs and EG-GRPO RL improve generative retrieval hit rates up to 26.76% over RQ-VAE baselines and deliver +1.15% GMV in live e-commerce A/B tests.
citing papers explorer
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Generative Retrieval Overcomes Limitations of Dense Retrieval but Struggles with Identifier Ambiguity
Generative retrieval beats dense retrieval and BM25 on the LIMIT dataset but degrades with hard negatives due to identifier ambiguity during decoding.
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Towards Efficient and Generalizable Retrieval: Adaptive Semantic Quantization and Residual Knowledge Transfer
SA²CRQ uses sequential adaptive residual quantization based on path entropy plus anchored curriculum regularization from head items to improve both efficiency and cold-start performance in generative retrieval.
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Efficient Generative Retrieval for E-commerce Search with Semantic Cluster IDs and Expert-Guided RL
CQ-SID semantic IDs and EG-GRPO RL improve generative retrieval hit rates up to 26.76% over RQ-VAE baselines and deliver +1.15% GMV in live e-commerce A/B tests.