Bounded-State Restoration: Decoupling Local Restore Capacity from External LLM State
该研究通过引入有界状态恢复（BSR）方法，解决了在不增加本地内存需求的情况下恢复大型语言模型执行状态的问题。
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Improving Dense Passage Retrieval with Multiple Positive Passages
Current dense passage retrieval (DPR) training paradigms typically rely on a single positive passage per query, overlooking the realistic many-to-one semantic relationship between a query and multiple relevant passages. This work presents the first systematic investigation into multi-positive training for DPR and proposes a multi-positive contrastive learning framework built upon the dual-encoder architecture: for each query, it jointly optimizes representations against all annotated positive passages, thereby strengthening negative contrast and enhancing semantic discriminability. Experiments demonstrate that our method significantly improves retrieval accuracy—achieving MRR@10 gains of 1.2–2.8 points—on standard benchmarks including MSMARCO and Natural Questions, without increasing GPU memory consumption. It maintains training stability even under small batch sizes, enabling efficient single-GPU training. The core contribution lies in empirically validating and realizing the effectiveness and practicality of multi-positive supervision for dense retrieval modeling.
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Bounded-State Restoration: Decoupling Local Restore Capacity from External LLM State
该研究通过引入有界状态恢复（BSR）方法，解决了在不增加本地内存需求的情况下恢复大型语言模型执行状态的问题。
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Improving Dense Passage Retrieval with Multiple Positive Passages
Current dense passage retrieval (DPR) training paradigms typically rely on a single positive passage per query, overlooking the realistic many-to-one semantic relationship between a query and multiple relevant passages. This work presents the first systematic investigation into multi-positive training for DPR and proposes a multi-positive contrastive learning framework built upon the dual-encoder architecture: for each query, it jointly optimizes representations against all annotated positive passages, thereby strengthening negative contrast and enhancing semantic discriminability. Experiments demonstrate that our method significantly improves retrieval accuracy—achieving MRR@10 gains of 1.2–2.8 points—on standard benchmarks including MSMARCO and Natural Questions, without increasing GPU memory consumption. It maintains training stability even under small batch sizes, enabling efficient single-GPU training. The core contribution lies in empirically validating and realizing the effectiveness and practicality of multi-positive supervision for dense retrieval modeling.
0 citationsRead paper