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data/1903.09940.json

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+{"paper_url": "https://huggingface.co/papers/1903.09940", "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* [Robust Overfitting Does Matter: Test-Time Adversarial Purification With FGSM](https://huggingface.co/papers/2403.11448) (2024)\n* [Exploring the Adversarial Frontier: Quantifying Robustness via Adversarial Hypervolume](https://huggingface.co/papers/2403.05100) (2024)\n* [Improving Adversarial Training using Vulnerability-Aware Perturbation Budget](https://huggingface.co/papers/2403.04070) (2024)\n* [Tighter Bounds on the Information Bottleneck with Application to Deep Learning](https://huggingface.co/papers/2402.07639) (2024)\n* [Robust optimization for adversarial learning with finite sample complexity guarantees](https://huggingface.co/papers/2403.15207) (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/1903.12248.json

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+{"paper_url": "https://huggingface.co/papers/1903.12248", "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* [VoxGenesis: Unsupervised Discovery of Latent Speaker Manifold for Speech Synthesis](https://huggingface.co/papers/2403.00529) (2024)\n* [A Closer Look at Wav2vec2 Embeddings for On-Device Single-Channel Speech Enhancement](https://huggingface.co/papers/2403.01369) (2024)\n* [Diffusion Models for Audio Restoration](https://huggingface.co/papers/2402.09821) (2024)\n* [PeriodGrad: Towards Pitch-Controllable Neural Vocoder Based on a Diffusion Probabilistic Model](https://huggingface.co/papers/2402.14692) (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/2112.00653.json

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+{"paper_url": "https://huggingface.co/papers/2112.00653", "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* [PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers](https://huggingface.co/papers/2402.08327) (2024)\n* [ARL2: Aligning Retrievers for Black-box Large Language Models via Self-guided Adaptive Relevance Labeling](https://huggingface.co/papers/2402.13542) (2024)\n* [Unsupervised Information Refinement Training of Large Language Models for Retrieval-Augmented Generation](https://huggingface.co/papers/2402.18150) (2024)\n* [RAFT: Adapting Language Model to Domain Specific RAG](https://huggingface.co/papers/2403.10131) (2024)\n* [M3: A Multi-Task Mixed-Objective Learning Framework for Open-Domain Multi-Hop Dense Sentence Retrieval](https://huggingface.co/papers/2403.14074) (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/2210.07295.json

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+{"paper_url": "https://huggingface.co/papers/2210.07295", "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* [A Knowledge Plug-and-Play Test Bed for Open-domain Dialogue Generation](https://huggingface.co/papers/2403.03496) (2024)\n* [Infusing Knowledge into Large Language Models with Contextual Prompts](https://huggingface.co/papers/2403.01481) (2024)\n* [Learning to Reduce: Optimal Representations of Structured Data in Prompting Large Language Models](https://huggingface.co/papers/2402.14195) (2024)\n* [SGSH: Stimulate Large Language Models with Skeleton Heuristics for Knowledge Base Question Generation](https://huggingface.co/papers/2404.01923) (2024)\n* [RAFT: Adapting Language Model to Domain Specific RAG](https://huggingface.co/papers/2403.10131) (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/2211.05100.json

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+{"paper_url": "https://huggingface.co/papers/2211.05100", "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* [Nemotron-4 15B Technical Report](https://huggingface.co/papers/2402.16819) (2024)\n* [Do Not Worry if You Do Not Have Data: Building Pretrained Language Models Using Translationese](https://huggingface.co/papers/2403.13638) (2024)\n* [Latxa: An Open Language Model and Evaluation Suite for Basque](https://huggingface.co/papers/2403.20266) (2024)\n* [Aurora-M: The First Open Source Multilingual Language Model Red-teamed according to the U.S. Executive Order](https://huggingface.co/papers/2404.00399) (2024)\n* [Gl\u00f3rIA: A Generative and Open Large Language Model for Portuguese](https://huggingface.co/papers/2402.12969) (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/2301.03988.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2301.03988", "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* [Evaluation of LLMs on Syntax-Aware Code Fill-in-the-Middle Tasks](https://huggingface.co/papers/2403.04814) (2024)\n* [CodeShell Technical Report](https://huggingface.co/papers/2403.15747) (2024)\n* [StarCoder 2 and The Stack v2: The Next Generation](https://huggingface.co/papers/2402.19173) (2024)\n* [EvoCodeBench: An Evolving Code Generation Benchmark Aligned with Real-World Code Repositories](https://huggingface.co/papers/2404.00599) (2024)\n* [Investigating the Performance of Language Models for Completing Code in Functional Programming Languages: a Haskell Case Study](https://huggingface.co/papers/2403.15185) (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/2305.06161.json

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+{"paper_url": "https://huggingface.co/papers/2305.06161", "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* [StarCoder 2 and The Stack v2: The Next Generation](https://huggingface.co/papers/2402.19173) (2024)\n* [Investigating the Performance of Language Models for Completing Code in Functional Programming Languages: a Haskell Case Study](https://huggingface.co/papers/2403.15185) (2024)\n* [CommitBench: A Benchmark for Commit Message Generation](https://huggingface.co/papers/2403.05188) (2024)\n* [CodeShell Technical Report](https://huggingface.co/papers/2403.15747) (2024)\n* [Evaluation of LLMs on Syntax-Aware Code Fill-in-the-Middle Tasks](https://huggingface.co/papers/2403.04814) (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/2305.11790.json

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+{"paper_url": "https://huggingface.co/papers/2305.11790", "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* [RoCoIns: Enhancing Robustness of Large Language Models through Code-Style Instructions](https://huggingface.co/papers/2402.16431) (2024)\n* [Fine-tuning Large Language Models with Sequential Instructions](https://huggingface.co/papers/2403.07794) (2024)\n* [Language Models for Text Classification: Is In-Context Learning Enough?](https://huggingface.co/papers/2403.17661) (2024)\n* [Eliciting Better Multilingual Structured Reasoning from LLMs through Code](https://huggingface.co/papers/2403.02567) (2024)\n* [Chain-of-Instructions: Compositional Instruction Tuning on Large Language Models](https://huggingface.co/papers/2402.11532) (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`"}