Selected work

Representative Papers

Edge-Aware Curvature Modeling for Graph Understanding in Large Language Models

Jun 04, 2026

Existing graph-aware large language models often neglect edge structure during graph-text alignment, leading to limited cross-modal information propagation and over-compression issues. This work proposes CureLLM, a novel framework that theoretically demonstrates for the first time that ignoring edge information results in suboptimal alignment. CureLLM introduces an innovative, training-free edge-aware textual prompting mechanism coupled with curvature-aware graph representation learning. By leveraging positively curved edges to guide message passing, the method effectively injects structural information into a frozen large language model. Evaluated on 11 real-world datasets, CureLLM significantly outperforms 20 baseline methods and achieves state-of-the-art performance in graph–text joint understanding tasks.

0 citationsRead paper

Retrieval Backward Attention without Additional Training: Enhance Embeddings of Large Language Models via Repetition

Feb 28, 2025

To address the insufficient quality and weak discriminability of pretrained language model (PLM) text embeddings in zero-shot settings, this paper proposes a fine-tuning-free Reverse Attention (RA) mechanism. RA leverages self-attention reconstruction and gradient backpropagation through the embedding layer to iteratively resample and reweight salient tokens, thereby enhancing contextual encoding fidelity in the embedding space. Crucially, RA requires no additional training—only standard forward inference—and thus preserves model efficiency and parameter integrity. Evaluated on the C-MTEB benchmark, RA consistently improves zero-shot performance across retrieval, classification, and other tasks, yielding average gains of 3.2%–5.7% over baseline embeddings. It significantly outperforms existing unsupervised embedding enhancement methods, establishing a new paradigm for efficient, lightweight zero-shot semantic representation.

0 citationsRead paper

Recent publications

Latest Papers

Edge-Aware Curvature Modeling for Graph Understanding in Large Language Models

Jun 04, 2026

Existing graph-aware large language models often neglect edge structure during graph-text alignment, leading to limited cross-modal information propagation and over-compression issues. This work proposes CureLLM, a novel framework that theoretically demonstrates for the first time that ignoring edge information results in suboptimal alignment. CureLLM introduces an innovative, training-free edge-aware textual prompting mechanism coupled with curvature-aware graph representation learning. By leveraging positively curved edges to guide message passing, the method effectively injects structural information into a frozen large language model. Evaluated on 11 real-world datasets, CureLLM significantly outperforms 20 baseline methods and achieves state-of-the-art performance in graph–text joint understanding tasks.

0 citationsRead paper

Retrieval Backward Attention without Additional Training: Enhance Embeddings of Large Language Models via Repetition

Feb 28, 2025

To address the insufficient quality and weak discriminability of pretrained language model (PLM) text embeddings in zero-shot settings, this paper proposes a fine-tuning-free Reverse Attention (RA) mechanism. RA leverages self-attention reconstruction and gradient backpropagation through the embedding layer to iteratively resample and reweight salient tokens, thereby enhancing contextual encoding fidelity in the embedding space. Crucially, RA requires no additional training—only standard forward inference—and thus preserves model efficiency and parameter integrity. Evaluated on the C-MTEB benchmark, RA consistently improves zero-shot performance across retrieval, classification, and other tasks, yielding average gains of 3.2%–5.7% over baseline embeddings. It significantly outperforms existing unsupervised embedding enhancement methods, establishing a new paradigm for efficient, lightweight zero-shot semantic representation.

0 citationsRead paper