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data/2401.17377.json

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+{"paper_url": "https://huggingface.co/papers/2401.17377", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Paloma: A Benchmark for Evaluating Language Model Fit](https://huggingface.co/papers/2312.10523) (2023)\n* [Rephrasing the Web: A Recipe for Compute and Data-Efficient Language Modeling](https://huggingface.co/papers/2401.16380) (2024)\n* [E^2-LLM: Efficient and Extreme Length Extension of Large Language Models](https://huggingface.co/papers/2401.06951) (2024)\n* [Non-Vacuous Generalization Bounds for Large Language Models](https://huggingface.co/papers/2312.17173) (2023)\n* [In-Context Language Learning: Architectures and Algorithms](https://huggingface.co/papers/2401.12973) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2401.17464.json

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+{"paper_url": "https://huggingface.co/papers/2401.17464", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [From Good to Great: Improving Math Reasoning with Tool-Augmented Interleaf Prompting](https://huggingface.co/papers/2401.05384) (2023)\n* [Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding](https://huggingface.co/papers/2401.04398) (2024)\n* [TAT-LLM: A Specialized Language Model for Discrete Reasoning over Tabular and Textual Data](https://huggingface.co/papers/2401.13223) (2024)\n* [EASYTOOL: Enhancing LLM-based Agents with Concise Tool Instruction](https://huggingface.co/papers/2401.06201) (2024)\n* [PathFinder: Guided Search over Multi-Step Reasoning Paths](https://huggingface.co/papers/2312.05180) (2023)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2401.17509.json

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+{"paper_url": "https://huggingface.co/papers/2401.17509", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [LightSim: Neural Lighting Simulation for Urban Scenes](https://huggingface.co/papers/2312.06654) (2023)\n* [Style-Consistent 3D Indoor Scene Synthesis with Decoupled Objects](https://huggingface.co/papers/2401.13203) (2024)\n* [Sat2Scene: 3D Urban Scene Generation from Satellite Images with Diffusion](https://huggingface.co/papers/2401.10786) (2024)\n* [Scene-Conditional 3D Object Stylization and Composition](https://huggingface.co/papers/2312.12419) (2023)\n* [3D Copy-Paste: Physically Plausible Object Insertion for Monocular 3D Detection](https://huggingface.co/papers/2312.05277) (2023)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2401.17574.json

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+{"paper_url": "https://huggingface.co/papers/2401.17574", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Understanding LLMs: A Comprehensive Overview from Training to Inference](https://huggingface.co/papers/2401.02038) (2024)\n* [Zebra: Extending Context Window with Layerwise Grouped Local-Global Attention](https://huggingface.co/papers/2312.08618) (2023)\n* [Engineering A Large Language Model From Scratch](https://huggingface.co/papers/2401.16736) (2024)\n* [DrBERT: Unveiling the Potential of Masked Language Modeling Decoder in BERT pretraining](https://huggingface.co/papers/2401.15861) (2024)\n* [PanGu-\u03c0: Enhancing Language Model Architectures via Nonlinearity Compensation](https://huggingface.co/papers/2312.17276) (2023)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2401.17895.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2401.17895", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [GO-NeRF: Generating Virtual Objects in Neural Radiance Fields](https://huggingface.co/papers/2401.05750) (2024)\n* [Inpaint4DNeRF: Promptable Spatio-Temporal NeRF Inpainting with Generative Diffusion Models](https://huggingface.co/papers/2401.00208) (2023)\n* [Inpaint3D: 3D Scene Content Generation using 2D Inpainting Diffusion](https://huggingface.co/papers/2312.03869) (2023)\n* [NeRFiller: Completing Scenes via Generative 3D Inpainting](https://huggingface.co/papers/2312.04560) (2023)\n* [InseRF: Text-Driven Generative Object Insertion in Neural 3D Scenes](https://huggingface.co/papers/2401.05335) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2401.18058.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2401.18058", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Zebra: Extending Context Window with Layerwise Grouped Local-Global Attention](https://huggingface.co/papers/2312.08618) (2023)\n* [Extending LLMs' Context Window with 100 Samples](https://huggingface.co/papers/2401.07004) (2024)\n* [E^2-LLM: Efficient and Extreme Length Extension of Large Language Models](https://huggingface.co/papers/2401.06951) (2024)\n* [INTERS: Unlocking the Power of Large Language Models in Search with Instruction Tuning](https://huggingface.co/papers/2401.06532) (2024)\n* [LLM Maybe LongLM: Self-Extend LLM Context Window Without Tuning](https://huggingface.co/papers/2401.01325) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2401.18059.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2401.18059", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Dense X Retrieval: What Retrieval Granularity Should We Use?](https://huggingface.co/papers/2312.06648) (2023)\n* [Retrieval-Augmented Generation for Large Language Models: A Survey](https://huggingface.co/papers/2312.10997) (2023)\n* [UniGen: A Unified Generative Framework for Retrieval and Question Answering with Large Language Models](https://huggingface.co/papers/2312.11036) (2023)\n* [CRUD-RAG: A Comprehensive Chinese Benchmark for Retrieval-Augmented Generation of Large Language Models](https://huggingface.co/papers/2401.17043) (2024)\n* [Re3val: Reinforced and Reranked Generative Retrieval](https://huggingface.co/papers/2401.16979) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2401.18075.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2401.18075", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [ProSGNeRF: Progressive Dynamic Neural Scene Graph with Frequency Modulated Auto-Encoder in Urban Scenes](https://huggingface.co/papers/2312.09076) (2023)\n* [ReconFusion: 3D Reconstruction with Diffusion Priors](https://huggingface.co/papers/2312.02981) (2023)\n* [Single-View 3D Human Digitalization with Large Reconstruction Models](https://huggingface.co/papers/2401.12175) (2024)\n* [Learning 3D Particle-based Simulators from RGB-D Videos](https://huggingface.co/papers/2312.05359) (2023)\n* [NVFi: Neural Velocity Fields for 3D Physics Learning from Dynamic Videos](https://huggingface.co/papers/2312.06398) (2023)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}