CaLIR learns continuous latent intent states guided by product category hierarchies for generative retrieval, combining hierarchical reasoning and dynamic prefix tries to balance effectiveness and low-latency inference on multilingual e-commerce data.
InProceedings of the 62nd Annual Meeting of the Association for Compu- tational Linguistics (Volume 3: System Demonstra- tions), Bangkok, Thailand
8 Pith papers cite this work, alongside 15 external citations. Polarity classification is still indexing.
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cs.IR 8representative citing papers
A product-key parametric memory head with selective sparse updates mitigates catastrophic forgetting in generative retrieval models during sequential addition of new documents.
DOME adapts generative IR models to unseen documents via critical-layer identification, hybrid-label edit vector optimization, and parameter updates, achieving strong new-document retrieval with reduced training cost.
ThinkGR interleaves chain-of-thought with docid generation using hybrid decoding and two-phase training to achieve state-of-the-art results on multi-hop retrieval benchmarks.
Reproduction confirms PAG boosts generative retrieval effectiveness, but its look-ahead planning signal collapses under intent-preserving typos and query mismatches, reverting performance to unguided decoding.
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.
HyPE improves generative retrieval by first generating hierarchical category paths for explainability and then using path-aware ranking to boost performance.
Presents a taxonomy of generative retrieval failures, empirically identifies issues such as ambiguous docids and low diversity in n-gram methods, and introduces a web-based debugging tool.
citing papers explorer
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Beyond Matching: Category-Guided Latent Intent Reasoning for Generative Retrieval in E-Commerce
CaLIR learns continuous latent intent states guided by product category hierarchies for generative retrieval, combining hierarchical reasoning and dynamic prefix tries to balance effectiveness and low-latency inference on multilingual e-commerce data.
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A Parametric Memory Head for Continual Generative Retrieval
A product-key parametric memory head with selective sparse updates mitigates catastrophic forgetting in generative retrieval models during sequential addition of new documents.
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Model Editing for New Document Integration in Generative Information Retrieval
DOME adapts generative IR models to unseen documents via critical-layer identification, hybrid-label edit vector optimization, and parameter updates, achieving strong new-document retrieval with reduced training cost.
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Integrating Chain-of-Thought into Generative Retrieval: A Preliminary Study
ThinkGR interleaves chain-of-thought with docid generation using hybrid decoding and two-phase training to achieve state-of-the-art results on multi-hop retrieval benchmarks.
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Lost in Decoding? Reproducing and Stress-Testing the Look-Ahead Prior in Generative Retrieval
Reproduction confirms PAG boosts generative retrieval effectiveness, but its look-ahead planning signal collapses under intent-preserving typos and query mismatches, reverting performance to unguided 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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Why These Documents? Explainable Generative Retrieval with Hierarchical Category Paths
HyPE improves generative retrieval by first generating hierarchical category paths for explainability and then using path-aware ranking to boost performance.
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Understanding and Debugging Failures in N-Gram-Based Generative Retrieval
Presents a taxonomy of generative retrieval failures, empirically identifies issues such as ambiguous docids and low diversity in n-gram methods, and introduces a web-based debugging tool.