Selected work

Representative Papers

What to Keep and What to Drop: Adaptive Table Filtering Framework

Jun 29, 2025

Large language models (LLMs) suffer from degraded table reasoning performance due to input-length constraints, hindering effective processing of long, wide tables. Method: We propose Adaptive Table Filtering (ATF), a plug-and-play framework that requires no model fine-tuning. ATF dynamically identifies and retains query-relevant table regions via question-aware column semantic description generation, hierarchical clustering, and sparse–dense vector alignment scoring. Its modular design enables cross-task adaptive balancing between information preservation and structural simplification. Contribution/Results: ATF prunes ~70% of table cells on average, significantly improving reasoning accuracy across diverse TableQA benchmarks. Only in rare cases requiring full-table structural understanding does performance marginally decline. Crucially, ATF is the first approach to jointly model LLM-driven semantic comprehension with interpretable, structured filtering—achieving strong efficiency, task-agnostic generalizability, and parameter-free transferability.

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ReCDAP: Relation-Based Conditional Diffusion with Attention Pooling for Few-Shot Knowledge Graph Completion

May 12, 2025

To address the degradation of few-shot link prediction performance caused by long-tail relations in knowledge graphs, this paper proposes Relation-Conditioned Diffusion with Attention Pooling (ReCDAP). ReCDAP introduces negative triples as structured signals into diffusion modeling for the first time, establishing a dual-path latent distribution framework that separately models positive and negative relations. It further employs a relation-aware attention pooling mechanism to explicitly capture discriminative differences between them. Integrated with few-shot meta-learning and negative sampling augmentation, ReCDAP jointly models both semantic and discriminative information of triples under sparse relations. Extensive experiments on FB15k-237 and NELL-995 demonstrate that ReCDAP significantly improves link prediction accuracy in few-shot settings, achieving state-of-the-art (SOTA) performance.

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Recent publications

Latest Papers

What to Keep and What to Drop: Adaptive Table Filtering Framework

Jun 29, 2025

Large language models (LLMs) suffer from degraded table reasoning performance due to input-length constraints, hindering effective processing of long, wide tables. Method: We propose Adaptive Table Filtering (ATF), a plug-and-play framework that requires no model fine-tuning. ATF dynamically identifies and retains query-relevant table regions via question-aware column semantic description generation, hierarchical clustering, and sparse–dense vector alignment scoring. Its modular design enables cross-task adaptive balancing between information preservation and structural simplification. Contribution/Results: ATF prunes ~70% of table cells on average, significantly improving reasoning accuracy across diverse TableQA benchmarks. Only in rare cases requiring full-table structural understanding does performance marginally decline. Crucially, ATF is the first approach to jointly model LLM-driven semantic comprehension with interpretable, structured filtering—achieving strong efficiency, task-agnostic generalizability, and parameter-free transferability.

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ReCDAP: Relation-Based Conditional Diffusion with Attention Pooling for Few-Shot Knowledge Graph Completion

May 12, 2025

To address the degradation of few-shot link prediction performance caused by long-tail relations in knowledge graphs, this paper proposes Relation-Conditioned Diffusion with Attention Pooling (ReCDAP). ReCDAP introduces negative triples as structured signals into diffusion modeling for the first time, establishing a dual-path latent distribution framework that separately models positive and negative relations. It further employs a relation-aware attention pooling mechanism to explicitly capture discriminative differences between them. Integrated with few-shot meta-learning and negative sampling augmentation, ReCDAP jointly models both semantic and discriminative information of triples under sparse relations. Extensive experiments on FB15k-237 and NELL-995 demonstrate that ReCDAP significantly improves link prediction accuracy in few-shot settings, achieving state-of-the-art (SOTA) performance.

0 citationsRead paper