·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Microsoft launches its first cybersecurity model, plus a new agentic cybersecurity system2h◆OpenAI called the Hugging Face attack unprecedented. But we’ve been here before.2h◆OpenAI’s Hugging Face breach has reignited the debate over alignment and control3h◆Why China is giving away its best AI models3h◆Threads users can now chat with Meta AI in their DMs3h◆Google’s AI search is rapidly becoming the default, new data shows4h◆Power up your AI infrastructure! A first look at the Smart Systems Stage agenda at TechCrunch Disrupt 20265h◆This $9 key physically locks your most addictive apps5h◆Ilya Sutskever’s Safe Superintelligence partners with Nvidia to scale its AI research5h◆Enigma raises $71M to make controlling a robot as easy as adjusting the volume7h◆Nvidia, Microsoft launch open AI security alliance — without OpenAI, Google, or Anthropic8h◆The path to artificial superintelligence8h◆Closing the data loop in AI-driven drug discovery9h◆Building the enterprise environment for agentic AI9h◆NVIDIA Cosmos-H-Dreams: Bringing Real-Time Generative Simulation to Surgical Robotics11h◆A Consensus-Based Framework for Relative Preference Evaluation of Large Language Models16h◆Probing Latent Colombian Identity Inferences in Qwen2.5-7B with Natural Language Autoencoders16h◆Data Quality over Capacity: Internalizing Documents into LoRA Adapters for Closed-Book QA16h◆Enjoy Your Talk: A Human-Centered Benchmark for Multi-Turn Dialogue with Decoupled User Simulation, Target Modeling, and Judging16h◆Multi-Mask Diffusion Language Models for Few-Step Generation16h◆Microsoft launches its first cybersecurity model, plus a new agentic cybersecurity system2h◆OpenAI called the Hugging Face attack unprecedented. But we’ve been here before.2h◆OpenAI’s Hugging Face breach has reignited the debate over alignment and control3h◆Why China is giving away its best AI models3h◆Threads users can now chat with Meta AI in their DMs3h◆Google’s AI search is rapidly becoming the default, new data shows4h◆Power up your AI infrastructure! A first look at the Smart Systems Stage agenda at TechCrunch Disrupt 20265h◆This $9 key physically locks your most addictive apps5h◆Ilya Sutskever’s Safe Superintelligence partners with Nvidia to scale its AI research5h◆Enigma raises $71M to make controlling a robot as easy as adjusting the volume7h◆Nvidia, Microsoft launch open AI security alliance — without OpenAI, Google, or Anthropic8h◆The path to artificial superintelligence8h◆Closing the data loop in AI-driven drug discovery9h◆Building the enterprise environment for agentic AI9h◆NVIDIA Cosmos-H-Dreams: Bringing Real-Time Generative Simulation to Surgical Robotics11h◆A Consensus-Based Framework for Relative Preference Evaluation of Large Language Models16h◆Probing Latent Colombian Identity Inferences in Qwen2.5-7B with Natural Language Autoencoders16h◆Data Quality over Capacity: Internalizing Documents into LoRA Adapters for Closed-Book QA16h◆Enjoy Your Talk: A Human-Centered Benchmark for Multi-Turn Dialogue with Decoupled User Simulation, Target Modeling, and Judging16h◆Multi-Mask Diffusion Language Models for Few-Step Generation16h◆
News/LLM-based Atomic Propositions help weak extractors: Evaluation of a Propositioner for triplet extraction
arxiv
PublishedApril 6, 2026 at 4:00 AM
—neutral

LLM-based Atomic Propositions help weak extractors: Evaluation of a Propositioner for triplet extraction

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2604.02866v1 Announce Type: new Abstract: Knowledge Graph construction from natural language requires extracting structured triplets from complex, information-dense sentences. In this paper, we investigate if the decomposition of text into atomic propositions (minimal, semantically autonomous

Stay posted· Newsletter

A 5-min weekly brief — top movers, price watch, story of the week.

// no spam · unsubscribe one-click · free forever

Discussion
Mentioned models
05
  • 01
    MPropositionneur-V2
  • 02
    Qwen3-32B
  • 03
    Qwen3-0.6B
  • 04
    GLiREL
  • 05
    CoreNLP
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#knowledge graph#natural language#multilingual#information extraction

No replies yet. Be first.

Mentioned models
05
  • 01
    MPropositionneur-V2
  • 02
    Qwen3-32B
  • 03
    Qwen3-0.6B
  • 04
    GLiREL
  • 05
    CoreNLP
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#knowledge graph#natural language#multilingual#information extraction

Related coverage

More from ARXIV
arxivA Consensus-Based Framework for Relative Preference Evaluation of Large Language Models16harxivProbing Latent Colombian Identity Inferences in Qwen2.5-7B with Natural Language Autoencoders16harxivData Quality over Capacity: Internalizing Documents into LoRA Adapters for Closed-Book QA16harxivEnjoy Your Talk: A Human-Centered Benchmark for Multi-Turn Dialogue with Decoupled User Simulation, Target Modeling, and Judging16h
The Bubble Brief
WEEKLY

Read knowledge graph insights every Tuesday — top movers, new releases, story of the week.

// no spam · unsubscribe one-click · free forever

Originally published on arxiv ↗
HomeModelsNews