DeSQ decomposes questions into atomic constraints, maps them to SPARQL fragments with placeholders, grounds the placeholders, and assembles complete queries, outperforming prior methods on four of five benchmarks.
Autoregressive entity retrieval
9 Pith papers cite this work, alongside 200 external citations. Polarity classification is still indexing.
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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.
CapsID uses probabilistic capsule routing and confidence-based termination to generate variable-length semantic IDs, improving recall by 9.6% over strong baselines with half the latency of dual-representation systems.
Loss-based pruning of training data to limit facts and flatten their frequency distribution enables a 110M-parameter GPT-2 model to memorize 1.3 times more entity facts than standard training, matching a 1.3B-parameter model on the full dataset.
MVIGER integrates complementary knowledge from diverse prompts and indices in generative recommenders via a variational model with learnable prior over latent sources, showing superior performance on three datasets.
G-DRAGON framework maps language commands to OSM coordinates via lightweight LLM for global planning and uses frontier exploration for local targets, outperforming baselines in simulation and completing real UGV person-search missions up to 500m.
MSR-MEL synthesizes instance-centric, group-level, lexical, and statistical evidence with LLMs and asymmetric teacher-student GNNs to outperform prior unsupervised methods on multimodal entity linking benchmarks.
OneRec unifies retrieval and ranking in a generative recommender using session-wise decoding and iterative DPO-based preference alignment, achieving real-world gains on Kuaishou.
citing papers explorer
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DeSQ: Decomposition-based SPARQL Query Generation
DeSQ decomposes questions into atomic constraints, maps them to SPARQL fragments with placeholders, grounds the placeholders, and assembles complete queries, outperforming prior methods on four of five benchmarks.
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TubiFM: Unified Item, Carousel, and Search Ranking for Streaming Discovery
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.
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CapsID: Soft-Routed Variable-Length Semantic IDs for Generative Recommendation
CapsID uses probabilistic capsule routing and confidence-based termination to generate variable-length semantic IDs, improving recall by 9.6% over strong baselines with half the latency of dual-representation systems.
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Cram Less to Fit More: Training Data Pruning Improves Memorization of Facts
Loss-based pruning of training data to limit facts and flatten their frequency distribution enables a 110M-parameter GPT-2 model to memorize 1.3 times more entity facts than standard training, matching a 1.3B-parameter model on the full dataset.
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MVIGER: Multi-View Variational Integration of Complementary Knowledge for Generative Recommender
MVIGER integrates complementary knowledge from diverse prompts and indices in generative recommenders via a variational model with learnable prior over latent sources, showing superior performance on three datasets.
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G-DRAGON: Geospatial Reasoning and Dynamic Planning for Retrieval-Augmented Outdoor Navigation
G-DRAGON framework maps language commands to OSM coordinates via lightweight LLM for global planning and uses frontier exploration for local targets, outperforming baselines in simulation and completing real UGV person-search missions up to 500m.
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Multi-Perspective Evidence Synthesis and Reasoning for Unsupervised Multimodal Entity Linking
MSR-MEL synthesizes instance-centric, group-level, lexical, and statistical evidence with LLMs and asymmetric teacher-student GNNs to outperform prior unsupervised methods on multimodal entity linking benchmarks.
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OneRec: Unifying Retrieve and Rank with Generative Recommender and Iterative Preference Alignment
OneRec unifies retrieval and ranking in a generative recommender using session-wise decoding and iterative DPO-based preference alignment, achieving real-world gains on Kuaishou.
- ICICLE: Expanding Retrieval with In-Context Documents