Active Indexing with synthetic data augmentation for bidirectional fact-source binding during pretraining yields up to 30.2% higher citation precision than passive identifier appending on CitePretrainBench for Qwen models.
HA- GRID: A human-llm collaborative dataset for generative information-seeking with attribution
2 Pith papers cite this work. Polarity classification is still indexing.
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ROS-LLM integrates LLMs with ROS to let non-experts specify robot tasks in natural language, supporting sequence, behavior tree, and state machine modes plus imitation learning and reflection on feedback.
citing papers explorer
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Cite Pretrain: Retrieval-Free Knowledge Attribution for Large Language Models
Active Indexing with synthetic data augmentation for bidirectional fact-source binding during pretraining yields up to 30.2% higher citation precision than passive identifier appending on CitePretrainBench for Qwen models.
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ROS-LLM: A ROS framework for embodied AI with task feedback and structured reasoning
ROS-LLM integrates LLMs with ROS to let non-experts specify robot tasks in natural language, supporting sequence, behavior tree, and state machine modes plus imitation learning and reflection on feedback.