Tag: Retrieval-Augmented Generation
All the articles with the tag "Retrieval-Augmented Generation".
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MaskSearch: A Universal Pre-Training Framework to Enhance Agentic Search Capability
Published: at 11:19 AM
87.72 🤔本文提出 MASKSEARCH 框架，通过 Retrieval-Augmented Mask Prediction (RAMP) 预训练任务结合监督微调和强化学习，显著提升了大型语言模型在开放域多跳问答任务中的代理搜索能力。
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ZeroSearch: Incentivize the Search Capability of LLMs without Searching
Published: at 06:16 PM 86.09 🤔ZEROSEARCH introduces a reinforcement learning framework that enhances LLMs' search capabilities by simulating search engines with fine-tuned LLMs, achieving performance comparable to or better than real search engines without API costs through a curriculum-based rollout strategy.
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Long Term Memory: The Foundation of AI Self-Evolution
Published: at 11:08 AM 85.57 🤔This paper proposes Long-Term Memory (LTM) as a cornerstone for AI self-evolution, demonstrating through multi-agent frameworks like OMNE and diverse experiments that LTM enables personalized, adaptive learning in LLMs during inference, achieving top performance on benchmarks like GAIA.
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Less is More: Enhancing Structured Multi-Agent Reasoning via Quality-Guided Distillation
Published: at 04:28 PM 77.44 🤔本文提出了一种质量导向的多代理框架，通过提示诱导、检索增强合成和奖励过滤从少量标注数据中提炼高质量监督信号，提升LLMs在低资源结构化推理任务中的性能。
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Direct Retrieval-augmented Optimization: Synergizing Knowledge Selection and Language Models
Published: at 11:14 AM 75.18 🤔This paper introduces Direct Retrieval-augmented Optimization (DRO), a framework that synergizes knowledge selection and LLM generation through end-to-end training using a variational approach, achieving 5-15% improvements in EM and F1 scores across five QA datasets.