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ENTITY Natural Questions

Natural Questions

PulseAugur coverage of Natural Questions — every cluster mentioning Natural Questions across labs, papers, and developer communities, ranked by signal.

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Total · 30d
5
12 over 90d
Releases · 30d
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Papers · 30d
4
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TIER MIX · 90D
TOPICS
SENTIMENT · 30D

4 day(s) with sentiment data

RECENT · PAGE 1/1 · 12 TOTAL
  1. RESEARCH · CL_193013 ·

    SAGE system optimizes RAG retrieval for latency and cost · 2 sources tracked

    Researchers have developed SAGE, a new adaptive retrieval policy for production Retrieval-Augmented Generation (RAG) systems. SAGE dynamically adjusts the number of passages retrieved per query based on estimated query …

  2. TOOL · CL_178938 ·

    TAPR refines LLM prompts to boost benchmark accuracy

    TAPR, a new system, automatically refines user prompts for large language models using reinforcement learning. This process enhances the accuracy of LLM responses, particularly on benchmarks like Natural Questions and GSM8K.

  3. RESEARCH · CL_167400 ·

    New RAG defense frameworks combat data poisoning attacks · 3 sources tracked

    Researchers have developed new defense frameworks to protect Retrieval-Augmented Generation (RAG) systems from data poisoning attacks. RAGuard, presented in two papers, employs a layered approach including adversarial r…

  4. RESEARCH · CL_147773 ·

    SmartRAG enables LLMs on mobile devices with graph-based RAG

    Researchers have developed SmartRAG, a novel on-device framework designed to enable large language models (LLMs) to function as personal assistants on mobile devices. This system decomposes intelligence into four module…

  5. TOOL · CL_132670 ·

    User distills DeepSeek V4 Pro into Gemma 26B MoE and 12B dense models

    A user detailed their process of distilling the DeepSeek V4 Pro model into two versions of Gemma: a 26B parameter MoE model and a 12B parameter dense model. The distillation process, which involved repopulating Natural …

  6. RESEARCH · CL_117090 ·

    New RAG research enhances LLM retrieval, unlearning, and faithfulness

    Multiple research papers are exploring advancements in retrieval-augmented generation (RAG) to improve the performance and efficiency of large language models. Apple's CLaRa framework unifies retrieval and generation in…

  7. TOOL · CL_93461 ·

    New indexing framework SPI boosts RAG performance in vector databases

    Researchers have introduced Semantic Pyramid Indexing (SPI), a novel indexing framework for vector databases designed to enhance retrieval-augmented generation (RAG) pipelines. SPI adapts the retrieval depth based on qu…

  8. TOOL · CL_93123 ·

    CONCORD framework enhances device-cloud RAG with asynchronous sparse aggregation

    Researchers have introduced CONCORD, a new framework designed to optimize retrieval-augmented generation (RAG) in a device-cloud collaborative setting where private documents are kept on local devices and public knowled…

  9. RESEARCH · CL_56340 ·

    SilentRetrieval attack hijacks RAG systems with poisoned documents

    Researchers have developed "SilentRetrieval," a novel two-stage attack designed to compromise Retrieval-Augmented Generation (RAG) systems. This method uses adversarial data poisoning to inject manipulated documents tha…

  10. RESEARCH · CL_30773 ·

    PersonalAI 2.0 enhances LLMs with knowledge graphs and planning

    Researchers have developed PersonalAI 2.0 (PAI-2), a new framework that improves large language model (LLM) systems by integrating external knowledge graphs. PAI-2 employs a dynamic, multistage query processing pipeline…

  11. TOOL · CL_22035 ·

    RAG system architectures show varied robustness to knowledge base poisoning

    Researchers have investigated the vulnerability of Retrieval-Augmented Generation (RAG) systems to knowledge base poisoning, finding that system architecture significantly impacts adversarial robustness. Evaluations on …

  12. RESEARCH · CL_06661 ·

    Researchers propose Parametric Memory Head to improve generative retrieval models

    Researchers have developed a novel approach called Post-Adaptation Memory Tuning (PAMT) to address the challenge of catastrophic forgetting in generative information retrieval models. PAMT introduces a modular parametri…