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data/2305.13648.json

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+{"paper_url": "https://huggingface.co/papers/2305.13648", "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* [The Fine-Tuning Paradox: Boosting Translation Quality Without Sacrificing LLM Abilities](https://huggingface.co/papers/2405.20089) (2024)\n* [Chasing COMET: Leveraging Minimum Bayes Risk Decoding for Self-Improving Machine Translation](https://huggingface.co/papers/2405.11937) (2024)\n* [Quality Estimation with k-nearest Neighbors and Automatic Evaluation for Model-specific Quality Estimation](https://huggingface.co/papers/2404.18031) (2024)\n* [Guiding Large Language Models to Post-Edit Machine Translation with Error Annotations](https://huggingface.co/papers/2404.07851) (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/2405.06640.json

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+{"paper_url": "https://huggingface.co/papers/2405.06640", "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* [RecurrentGemma: Moving Past Transformers for Efficient Open Language Models](https://huggingface.co/papers/2404.07839) (2024)\n* [HGRN2: Gated Linear RNNs with State Expansion](https://huggingface.co/papers/2404.07904) (2024)\n* [Zamba: A Compact 7B SSM Hybrid Model](https://huggingface.co/papers/2405.16712) (2024)\n* [Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence](https://huggingface.co/papers/2404.05892) (2024)\n* [Megalodon: Efficient LLM Pretraining and Inference with Unlimited Context Length](https://huggingface.co/papers/2404.08801) (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/2405.18377.json

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+{"paper_url": "https://huggingface.co/papers/2405.18377", "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* [Structural Pruning of Pre-trained Language Models via Neural Architecture Search](https://huggingface.co/papers/2405.02267) (2024)\n* [Enabling High-Sparsity Foundational Llama Models with Efficient Pretraining and Deployment](https://huggingface.co/papers/2405.03594) (2024)\n* [What Happens When Small Is Made Smaller? Exploring the Impact of Compression on Small Data Pretrained Language Models](https://huggingface.co/papers/2404.04759) (2024)\n* [Pre-training Small Base LMs with Fewer Tokens](https://huggingface.co/papers/2404.08634) (2024)\n* [MixLoRA: Enhancing Large Language Models Fine-Tuning with LoRA based Mixture of Experts](https://huggingface.co/papers/2404.15159) (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/2405.18426.json

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+{"paper_url": "https://huggingface.co/papers/2405.18426", "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* [DreamScene4D: Dynamic Multi-Object Scene Generation from Monocular Videos](https://huggingface.co/papers/2405.02280) (2024)\n* [DeblurGS: Gaussian Splatting for Camera Motion Blur](https://huggingface.co/papers/2404.11358) (2024)\n* [A Construct-Optimize Approach to Sparse View Synthesis without Camera Pose](https://huggingface.co/papers/2405.03659) (2024)\n* [Guess The Unseen: Dynamic 3D Scene Reconstruction from Partial 2D Glimpses](https://huggingface.co/papers/2404.14410) (2024)\n* [Memorize What Matters: Emergent Scene Decomposition from Multitraverse](https://huggingface.co/papers/2405.17187) (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/2405.19313.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2405.19313", "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* [Large Language Models are Biased Reinforcement Learners](https://huggingface.co/papers/2405.11422) (2024)\n* [Would I Lie To You? Inference Time Alignment of Language Models using Direct Preference Heads](https://huggingface.co/papers/2405.20053) (2024)\n* [Exploring the LLM Journey from Cognition to Expression with Linear Representations](https://huggingface.co/papers/2405.16964) (2024)\n* [Matching domain experts by training from scratch on domain knowledge](https://huggingface.co/papers/2405.09395) (2024)\n* [RLHF Deciphered: A Critical Analysis of Reinforcement Learning from Human Feedback for LLMs](https://huggingface.co/papers/2404.08555) (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/2405.20204.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2405.20204", "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* [Enhancing Vision-Language Model with Unmasked Token Alignment](https://huggingface.co/papers/2405.19009) (2024)\n* [RankCLIP: Ranking-Consistent Language-Image Pretraining](https://huggingface.co/papers/2404.09387) (2024)\n* [A Progressive Framework of Vision-language Knowledge Distillation and Alignment for Multilingual Scene](https://huggingface.co/papers/2404.11249) (2024)\n* [Piccolo2: General Text Embedding with Multi-task Hybrid Loss Training](https://huggingface.co/papers/2405.06932) (2024)\n* [Modeling Caption Diversity in Contrastive Vision-Language Pretraining](https://huggingface.co/papers/2405.00740) (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`"}