✨Adapting Web Agents with Synthetic Supervision
📝 Summary:
SynthAgent enhances web agent adaptation by improving synthetic data quality. It refines synthesized tasks and cleans collected trajectories to prevent hallucinations and noise. This dual refinement approach enables better performance on new websites.
🔹 Publication Date: Published on Nov 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.06101
• PDF: https://arxiv.org/pdf/2511.06101
• Project Page: https://github.com/aiming-lab/SynthAgent
• Github: https://github.com/aiming-lab/SynthAgent
==================================
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#WebAgents #SyntheticData #MachineLearning #AIResearch #DataQuality
📝 Summary:
SynthAgent enhances web agent adaptation by improving synthetic data quality. It refines synthesized tasks and cleans collected trajectories to prevent hallucinations and noise. This dual refinement approach enables better performance on new websites.
🔹 Publication Date: Published on Nov 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.06101
• PDF: https://arxiv.org/pdf/2511.06101
• Project Page: https://github.com/aiming-lab/SynthAgent
• Github: https://github.com/aiming-lab/SynthAgent
==================================
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#WebAgents #SyntheticData #MachineLearning #AIResearch #DataQuality
✨GraphGen: Enhancing Supervised Fine-Tuning for LLMs with Knowledge-Driven Synthetic Data Generation
📝 Summary:
GraphGen is a framework that enhances synthetic data generation for LLMs by constructing fine-grained knowledge graphs. It targets high-value knowledge gaps, uses multi-hop sampling, and style-controlled generation to create diverse and accurate QA pairs. This approach outperforms conventional me...
🔹 Publication Date: Published on May 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2505.20416
• PDF: https://arxiv.org/pdf/2505.20416
• Project Page: https://huggingface.co/spaces/chenzihong/GraphGen
• Github: https://github.com/open-sciencelab/GraphGen
✨ Datasets citing this paper:
• https://huggingface.co/datasets/chenzihong/GraphGen-Data
✨ Spaces citing this paper:
• https://huggingface.co/spaces/chenzihong/GraphGen
==================================
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#LLMs #KnowledgeGraphs #SyntheticData #FineTuning #NLP
📝 Summary:
GraphGen is a framework that enhances synthetic data generation for LLMs by constructing fine-grained knowledge graphs. It targets high-value knowledge gaps, uses multi-hop sampling, and style-controlled generation to create diverse and accurate QA pairs. This approach outperforms conventional me...
🔹 Publication Date: Published on May 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2505.20416
• PDF: https://arxiv.org/pdf/2505.20416
• Project Page: https://huggingface.co/spaces/chenzihong/GraphGen
• Github: https://github.com/open-sciencelab/GraphGen
✨ Datasets citing this paper:
• https://huggingface.co/datasets/chenzihong/GraphGen-Data
✨ Spaces citing this paper:
• https://huggingface.co/spaces/chenzihong/GraphGen
==================================
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#LLMs #KnowledgeGraphs #SyntheticData #FineTuning #NLP
✨Taming Generative Synthetic Data for X-ray Prohibited Item Detection
📝 Summary:
Xsyn introduces a one-stage text-to-image synthesis pipeline for X-ray security images. It eliminates labor costs and improves image quality and efficiency for training detection models. This method significantly enhances prohibited item detection performance, outperforming prior approaches.
🔹 Publication Date: Published on Nov 19
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.15299
• PDF: https://arxiv.org/pdf/2511.15299
• Github: https://github.com/pILLOW-1/Xsyn/
==================================
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#XraySecurity #GenerativeAI #ComputerVision #SyntheticData #ObjectDetection
📝 Summary:
Xsyn introduces a one-stage text-to-image synthesis pipeline for X-ray security images. It eliminates labor costs and improves image quality and efficiency for training detection models. This method significantly enhances prohibited item detection performance, outperforming prior approaches.
🔹 Publication Date: Published on Nov 19
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.15299
• PDF: https://arxiv.org/pdf/2511.15299
• Github: https://github.com/pILLOW-1/Xsyn/
==================================
For more data science resources:
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#XraySecurity #GenerativeAI #ComputerVision #SyntheticData #ObjectDetection