MulTaBench is a new collection of 40 image-tabular and text-tabular datasets designed to test target-aware representation tuning in multimodal tabular models.
QUEST: A Retrieval Dataset of Entity-Seeking Queries with Implicit Set Operations
4 Pith papers cite this work, alongside 11 external citations. Polarity classification is still indexing.
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Neural retrievers that double BM25 performance on QUEST collapse below 0.02 Recall@100 on the new LIMIT+ benchmark while lexical methods reach 0.96, with all methods degrading as compositional depth increases.
SPADER is an RL method for multi-answer QA that claims better recall and F1 via peer-aligned step-level advantages and diversity rewards on four benchmarks.
NSFL adapts t-norms and t-conorms to embedding spaces with NS-Delta and SQO to enable logical operations, reporting up to 81% mAP gains in retrieval tasks.
citing papers explorer
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MulTaBench: Benchmarking Multimodal Tabular Learning with Text and Image
MulTaBench is a new collection of 40 image-tabular and text-tabular datasets designed to test target-aware representation tuning in multimodal tabular models.
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Reproducing Complex Set-Compositional Information Retrieval
Neural retrievers that double BM25 performance on QUEST collapse below 0.02 Recall@100 on the new LIMIT+ benchmark while lexical methods reach 0.96, with all methods degrading as compositional depth increases.
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SPADER: Step-wise Peer Advantage with Diversity-Aware Exploration Rewards for Multi-Answer Question Answering
SPADER is an RL method for multi-answer QA that claims better recall and F1 via peer-aligned step-level advantages and diversity rewards on four benchmarks.
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NSFL: A Post-Training Neuro-Symbolic Fuzzy Logic Framework for Boolean Operators in Neural Embeddings
NSFL adapts t-norms and t-conorms to embedding spaces with NS-Delta and SQO to enable logical operations, reporting up to 81% mAP gains in retrieval tasks.