Selected work

Representative Papers

Few-Shot, No Problem: Descriptive Continual Relation Extraction

Feb 27, 2025

Few-shot continual relation extraction suffers from catastrophic forgetting and difficulty in knowledge consolidation, especially under data scarcity, where modeling relational semantics and mitigating overfitting remain challenging. To address this, we propose the first description-driven retrieval-based continual learning paradigm: leveraging large language models to generate structured relational descriptions, constructing a dual-encoder retrieval framework that jointly encodes class prototypes and semantic descriptions, and designing a reciprocal rank fusion (RRF)-based prediction mechanism for robust inference. Our method achieves significant improvements over state-of-the-art approaches across multiple benchmarks, demonstrating superior stability, strong generalization capability, and effective forgetting mitigation. It establishes a novel paradigm for continual relation learning in low-resource, dynamic environments.

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CoT2Align: Cross-Chain of Thought Distillation via Optimal Transport Alignment for Language Models with Different Tokenizers

Feb 24, 2025

To address insufficient reasoning capability transfer in large-model knowledge distillation under heterogeneous tokenizers, this paper proposes a general distillation framework integrating Chain-of-Thought (CoT) enhancement and cross-chain alignment. The framework introduces a novel cross-chain CoT alignment mechanism, extending optimal transport to both sequence-level and layer-level matching while preserving variable-length input handling and contextual integrity. It jointly models CoT-enhanced reasoning, heterogeneous vocabulary mapping, and sequence-layer coupled distillation. Under diverse vocabulary configurations, our method consistently outperforms baselines—including ULD and DSKD—on reasoning tasks and domain robustness. Empirical results demonstrate substantial improvements across multiple benchmarks, establishing a new paradigm for efficient knowledge transfer in tokenizer-agnostic distillation scenarios.

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Few-shot Continual Relation Extraction via Open Information Extraction

Feb 23, 2025

To address three key challenges in few-shot continual relation extraction (FCRE)—catastrophic forgetting, scarcity of labeled instances for novel relations, and difficulty in identifying unseen relations—this paper proposes the first continual learning framework integrating Open Information Extraction (OpenIE) with dynamic Knowledge Graph Construction (KGC). Methodologically, it leverages OpenIE to automatically extract relational triples and incrementally expand the knowledge graph; a few-shot adaptation module enables rapid generalization to new relations, while structural constraints imposed by the graph ensure retention of prior-task knowledge. Innovatively, the framework incorporates open-domain relation discovery into continual learning, supporting zero-shot recognition of unseen relations and enabling dynamic graph evolution. Evaluated on standard FCRE benchmarks, it significantly outperforms state-of-the-art methods, demonstrating superior cross-task knowledge stability and strong generalization capability to previously unobserved relations.

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Recent publications

Latest Papers

Few-Shot, No Problem: Descriptive Continual Relation Extraction

Feb 27, 2025

Few-shot continual relation extraction suffers from catastrophic forgetting and difficulty in knowledge consolidation, especially under data scarcity, where modeling relational semantics and mitigating overfitting remain challenging. To address this, we propose the first description-driven retrieval-based continual learning paradigm: leveraging large language models to generate structured relational descriptions, constructing a dual-encoder retrieval framework that jointly encodes class prototypes and semantic descriptions, and designing a reciprocal rank fusion (RRF)-based prediction mechanism for robust inference. Our method achieves significant improvements over state-of-the-art approaches across multiple benchmarks, demonstrating superior stability, strong generalization capability, and effective forgetting mitigation. It establishes a novel paradigm for continual relation learning in low-resource, dynamic environments.

0 citationsRead paper

CoT2Align: Cross-Chain of Thought Distillation via Optimal Transport Alignment for Language Models with Different Tokenizers

Feb 24, 2025

To address insufficient reasoning capability transfer in large-model knowledge distillation under heterogeneous tokenizers, this paper proposes a general distillation framework integrating Chain-of-Thought (CoT) enhancement and cross-chain alignment. The framework introduces a novel cross-chain CoT alignment mechanism, extending optimal transport to both sequence-level and layer-level matching while preserving variable-length input handling and contextual integrity. It jointly models CoT-enhanced reasoning, heterogeneous vocabulary mapping, and sequence-layer coupled distillation. Under diverse vocabulary configurations, our method consistently outperforms baselines—including ULD and DSKD—on reasoning tasks and domain robustness. Empirical results demonstrate substantial improvements across multiple benchmarks, establishing a new paradigm for efficient knowledge transfer in tokenizer-agnostic distillation scenarios.

0 citationsRead paper

Few-shot Continual Relation Extraction via Open Information Extraction

Feb 23, 2025

To address three key challenges in few-shot continual relation extraction (FCRE)—catastrophic forgetting, scarcity of labeled instances for novel relations, and difficulty in identifying unseen relations—this paper proposes the first continual learning framework integrating Open Information Extraction (OpenIE) with dynamic Knowledge Graph Construction (KGC). Methodologically, it leverages OpenIE to automatically extract relational triples and incrementally expand the knowledge graph; a few-shot adaptation module enables rapid generalization to new relations, while structural constraints imposed by the graph ensure retention of prior-task knowledge. Innovatively, the framework incorporates open-domain relation discovery into continual learning, supporting zero-shot recognition of unseen relations and enabling dynamic graph evolution. Evaluated on standard FCRE benchmarks, it significantly outperforms state-of-the-art methods, demonstrating superior cross-task knowledge stability and strong generalization capability to previously unobserved relations.

0 citationsRead paper