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data/2501.14334.json

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+{"paper_url": "https://huggingface.co/papers/2501.14334", "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* [Towards an Environmental Ethics of Artificial Intelligence](https://huggingface.co/papers/2501.10390) (2024)\n* [A Survey of Sustainability in Large Language Models: Applications, Economics, and Challenges](https://huggingface.co/papers/2412.04782) (2024)\n* [Addressing the sustainable AI trilemma: a case study on LLM agents and RAG](https://huggingface.co/papers/2501.08262) (2025)\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/2501.14417.json

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+{"paper_url": "https://huggingface.co/papers/2501.14417", "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* [Hierarchical Autoscaling for Large Language Model Serving with Chiron](https://huggingface.co/papers/2501.08090) (2025)\n* [KunServe: Elastic and Efficient Large Language Model Serving with Parameter-centric Memory Management](https://huggingface.co/papers/2412.18169) (2024)\n* [Locality-aware Fair Scheduling in LLM Serving](https://huggingface.co/papers/2501.14312) (2025)\n* [EchoLM: Accelerating LLM Serving with Real-time Knowledge Distillation](https://huggingface.co/papers/2501.12689) (2025)\n* [Efficient Deployment of Large Language Models on Resource-constrained Devices](https://huggingface.co/papers/2501.02438) (2025)\n* [Rethinking cloud abstractions for tenant-provider cooperative optimization of AI workloads](https://huggingface.co/papers/2501.09562) (2025)\n* [A System for Microserving of LLMs](https://huggingface.co/papers/2412.12488) (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/2501.15654.json

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+{"paper_url": "https://huggingface.co/papers/2501.15654", "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* [Overview of the 2024 ALTA Shared Task: Detect Automatic AI-Generated Sentences for Human-AI Hybrid Articles](https://huggingface.co/papers/2412.17848) (2024)\n* [DAMAGE: Detecting Adversarially Modified AI Generated Text](https://huggingface.co/papers/2501.03437) (2025)\n* [Is This You, LLM? Recognizing AI-written Programs with Multilingual Code Stylometry](https://huggingface.co/papers/2412.14611) (2024)\n* [ExPerT: Effective and Explainable Evaluation of Personalized Long-Form Text Generation](https://huggingface.co/papers/2501.14956) (2025)\n* [Advancing LLM detection in the ALTA 2024 Shared Task: Techniques and Analysis](https://huggingface.co/papers/2412.19076) (2024)\n* [GenAI Content Detection Task 3: Cross-Domain Machine-Generated Text Detection Challenge](https://huggingface.co/papers/2501.08913) (2025)\n* [Using Machine Learning to Distinguish Human-written from Machine-generated Creative Fiction](https://huggingface.co/papers/2412.15253) (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/2501.15747.json

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+{"paper_url": "https://huggingface.co/papers/2501.15747", "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* [Setting Standards in Turkish NLP: TR-MMLU for Large Language Model Evaluation](https://huggingface.co/papers/2501.00593) (2024)\n* [Enabling Low-Resource Language Retrieval: Establishing Baselines for Urdu MS MARCO](https://huggingface.co/papers/2412.12997) (2024)\n* [When LLMs Struggle: Reference-less Translation Evaluation for Low-resource Languages](https://huggingface.co/papers/2501.04473) (2025)\n* [Can xLLMs Understand the Structure of Dialog? Exploring Multilingual Response Generation in Complex Scenarios](https://huggingface.co/papers/2501.11269) (2025)\n* [A Review of the Marathi Natural Language Processing](https://huggingface.co/papers/2412.15471) (2024)\n* [Can Large Language Models Predict the Outcome of Judicial Decisions?](https://huggingface.co/papers/2501.09768) (2025)\n* [MIT-10M: A Large Scale Parallel Corpus of Multilingual Image Translation](https://huggingface.co/papers/2412.07147) (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/2501.15891.json

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+{"paper_url": "https://huggingface.co/papers/2501.15891", "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* [IPVTON: Image-based 3D Virtual Try-on with Image Prompt Adapter](https://huggingface.co/papers/2501.15616) (2025)\n* [CatV2TON: Taming Diffusion Transformers for Vision-Based Virtual Try-On with Temporal Concatenation](https://huggingface.co/papers/2501.11325) (2025)\n* [PromptDresser: Improving the Quality and Controllability of Virtual Try-On via Generative Textual Prompt and Prompt-aware Mask](https://huggingface.co/papers/2412.16978) (2024)\n* [PEMF-VVTO: Point-Enhanced Video Virtual Try-on via Mask-free Paradigm](https://huggingface.co/papers/2412.03021) (2024)\n* [SwiftTry: Fast and Consistent Video Virtual Try-On with Diffusion Models](https://huggingface.co/papers/2412.10178) (2024)\n* [TryOffAnyone: Tiled Cloth Generation from a Dressed Person](https://huggingface.co/papers/2412.08573) (2024)\n* [ITVTON:Virtual Try-On Diffusion Transformer Model Based on Integrated Image and Text](https://huggingface.co/papers/2501.16757) (2025)\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/2501.16372.json

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+{"paper_url": "https://huggingface.co/papers/2501.16372", "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* [Gradient Weight-normalized Low-rank Projection for Efficient LLM Training](https://huggingface.co/papers/2412.19616) (2024)\n* [SaLoRA: Safety-Alignment Preserved Low-Rank Adaptation](https://huggingface.co/papers/2501.01765) (2025)\n* [Efficient Deployment of Large Language Models on Resource-constrained Devices](https://huggingface.co/papers/2501.02438) (2025)\n* [FlexiGPT: Pruning and Extending Large Language Models with Low-Rank Weight Sharing](https://huggingface.co/papers/2501.14713) (2025)\n* [ASLoRA: Adaptive Sharing Low-Rank Adaptation Across Layers](https://huggingface.co/papers/2412.10135) (2024)\n* [LoRS: Efficient Low-Rank Adaptation for Sparse Large Language Model](https://huggingface.co/papers/2501.08582) (2025)\n* [Low-Rank Adaptation for Foundation Models: A Comprehensive Review](https://huggingface.co/papers/2501.00365) (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/2501.16496.json

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+{"paper_url": "https://huggingface.co/papers/2501.16496", "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* [Interpretability in Parameter Space: Minimizing Mechanistic Description Length with Attribution-based Parameter Decomposition](https://huggingface.co/papers/2501.14926) (2025)\n* [MechIR: A Mechanistic Interpretability Framework for Information Retrieval](https://huggingface.co/papers/2501.10165) (2025)\n* [A Comprehensive Survey on Self-Interpretable Neural Networks](https://huggingface.co/papers/2501.15638) (2025)\n* [In Defence of Post-hoc Explainability](https://huggingface.co/papers/2412.17883) (2024)\n* [From Explainability to Interpretability: Interpretable Policies in Reinforcement Learning Via Model Explanation](https://huggingface.co/papers/2501.09858) (2025)\n* [Explainability in Neural Networks for Natural Language Processing Tasks](https://huggingface.co/papers/2412.18036) (2024)\n* [Neural Probabilistic Circuits: Enabling Compositional and Interpretable Predictions through Logical Reasoning](https://huggingface.co/papers/2501.07021) (2025)\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/2501.16764.json

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+{"paper_url": "https://huggingface.co/papers/2501.16764", "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* [Zero-1-to-G: Taming Pretrained 2D Diffusion Model for Direct 3D Generation](https://huggingface.co/papers/2501.05427) (2025)\n* [Gen-3Diffusion: Realistic Image-to-3D Generation via 2D & 3D Diffusion Synergy](https://huggingface.co/papers/2412.06698) (2024)\n* [CubeDiff: Repurposing Diffusion-Based Image Models for Panorama Generation](https://huggingface.co/papers/2501.17162) (2025)\n* [PartGen: Part-level 3D Generation and Reconstruction with Multi-View Diffusion Models](https://huggingface.co/papers/2412.18608) (2024)\n* [3DEnhancer: Consistent Multi-View Diffusion for 3D Enhancement](https://huggingface.co/papers/2412.18565) (2024)\n* [MEt3R: Measuring Multi-View Consistency in Generated Images](https://huggingface.co/papers/2501.06336) (2025)\n* [DSplats: 3D Generation by Denoising Splats-Based Multiview Diffusion Models](https://huggingface.co/papers/2412.09648) (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/2501.16937.json

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+{"paper_url": "https://huggingface.co/papers/2501.16937", "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* [Multi-Level Optimal Transport for Universal Cross-Tokenizer Knowledge Distillation on Language Models](https://huggingface.co/papers/2412.14528) (2024)\n* [Neural Collapse Inspired Knowledge Distillation](https://huggingface.co/papers/2412.11788) (2024)\n* [Knowledge distillation with adapted weight](https://huggingface.co/papers/2501.02705) (2025)\n* [Self-Evolution Knowledge Distillation for LLM-based Machine Translation](https://huggingface.co/papers/2412.15303) (2024)\n* [In-Context Learning Distillation for Efficient Few-Shot Fine-Tuning](https://huggingface.co/papers/2412.13243) (2024)\n* [LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering](https://huggingface.co/papers/2412.09807) (2024)\n* [Rethinking Knowledge in Distillation: An In-context Sample Retrieval Perspective](https://huggingface.co/papers/2501.07040) (2025)\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/2501.16975.json

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+{"paper_url": "https://huggingface.co/papers/2501.16975", "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* [Scaling Law for Language Models Training Considering Batch Size](https://huggingface.co/papers/2412.01505) (2024)\n* [FTP: A Fine-grained Token-wise Pruner for Large Language Models via Token Routing](https://huggingface.co/papers/2412.11494) (2024)\n* [ScaMo: Exploring the Scaling Law in Autoregressive Motion Generation Model](https://huggingface.co/papers/2412.14559) (2024)\n* [Byte Latent Transformer: Patches Scale Better Than Tokens](https://huggingface.co/papers/2412.09871) (2024)\n* [Chunk-Distilled Language Modeling](https://huggingface.co/papers/2501.00343) (2024)\n* [Retrieval-Augmented Semantic Parsing: Using Large Language Models to Improve Generalization](https://huggingface.co/papers/2412.10207) (2024)\n* [Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models](https://huggingface.co/papers/2501.10322) (2025)\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/2501.17116.json

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+{"paper_url": "https://huggingface.co/papers/2501.17116", "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* [GQSA: Group Quantization and Sparsity for Accelerating Large Language Model Inference](https://huggingface.co/papers/2412.17560) (2024)\n* [Scaling Laws for Floating Point Quantization Training](https://huggingface.co/papers/2501.02423) (2025)\n* [OstQuant: Refining Large Language Model Quantization with Orthogonal and Scaling Transformations for Better Distribution Fitting](https://huggingface.co/papers/2501.13987) (2025)\n* [CPTQuant - A Novel Mixed Precision Post-Training Quantization Techniques for Large Language Models](https://huggingface.co/papers/2412.03599) (2024)\n* [QPruner: Probabilistic Decision Quantization for Structured Pruning in Large Language Models](https://huggingface.co/papers/2412.11629) (2024)\n* [SKIM: Any-bit Quantization Pushing The Limits of Post-Training Quantization](https://huggingface.co/papers/2412.04180) (2024)\n* [RILQ: Rank-Insensitive LoRA-based Quantization Error Compensation for Boosting 2-bit Large Language Model Accuracy](https://huggingface.co/papers/2412.01129) (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/2501.17117.json

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+{"paper_url": "https://huggingface.co/papers/2501.17117", "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* [Whose Morality Do They Speak? Unraveling Cultural Bias in Multilingual Language Models](https://huggingface.co/papers/2412.18863) (2024)\n* [mGeNTE: A Multilingual Resource for Gender-Neutral Language and Translation](https://huggingface.co/papers/2501.09409) (2025)\n* [Exploring Large Language Models on Cross-Cultural Values in Connection with Training Methodology](https://huggingface.co/papers/2412.08846) (2024)\n* [SLAM: Towards Efficient Multilingual Reasoning via Selective Language Alignment](https://huggingface.co/papers/2501.03681) (2025)\n* [Cultural Palette: Pluralising Culture Alignment via Multi-agent Palette](https://huggingface.co/papers/2412.11167) (2024)\n* [The Roles of English in Evaluating Multilingual Language Models](https://huggingface.co/papers/2412.08392) (2024)\n* [AraSTEM: A Native Arabic Multiple Choice Question Benchmark for Evaluating LLMs Knowledge In STEM Subjects](https://huggingface.co/papers/2501.00559) (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/2501.17161.json

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+{"paper_url": "https://huggingface.co/papers/2501.17161", "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* [Advancing Language Model Reasoning through Reinforcement Learning and Inference Scaling](https://huggingface.co/papers/2501.11651) (2025)\n* [Reinforcement Learning Enhanced LLMs: A Survey](https://huggingface.co/papers/2412.10400) (2024)\n* [OpenRFT: Adapting Reasoning Foundation Model for Domain-specific Tasks with Reinforcement Fine-Tuning](https://huggingface.co/papers/2412.16849) (2024)\n* [GePBench: Evaluating Fundamental Geometric Perception for Multimodal Large Language Models](https://huggingface.co/papers/2412.21036) (2024)\n* [Activating Distributed Visual Region within LLMs for Efficient and Effective Vision-Language Training and Inference](https://huggingface.co/papers/2412.12785) (2024)\n* [Diving into Self-Evolving Training for Multimodal Reasoning](https://huggingface.co/papers/2412.17451) (2024)\n* [Kimi k1.5: Scaling Reinforcement Learning with LLMs](https://huggingface.co/papers/2501.12599) (2025)\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/2501.17195.json

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+{"paper_url": "https://huggingface.co/papers/2501.17195", "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* [Tarsier2: Advancing Large Vision-Language Models from Detailed Video Description to Comprehensive Video Understanding](https://huggingface.co/papers/2501.07888) (2025)\n* [GLIDER: Grading LLM Interactions and Decisions using Explainable Ranking](https://huggingface.co/papers/2412.14140) (2024)\n* [PromptOptMe: Error-Aware Prompt Compression for LLM-based MT Evaluation Metrics](https://huggingface.co/papers/2412.16120) (2024)\n* [RobustFT: Robust Supervised Fine-tuning for Large Language Models under Noisy Response](https://huggingface.co/papers/2412.14922) (2024)\n* [Comparing Few-Shot Prompting of GPT-4 LLMs with BERT Classifiers for Open-Response Assessment in Tutor Equity Training](https://huggingface.co/papers/2501.06658) (2025)\n* [Multimodal Preference Data Synthetic Alignment with Reward Model](https://huggingface.co/papers/2412.17417) (2024)\n* [Scalable Vision Language Model Training via High Quality Data Curation](https://huggingface.co/papers/2501.05952) (2025)\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/2501.17433.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2501.17433", "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* [Look Before You Leap: Enhancing Attention and Vigilance Regarding Harmful Content with GuidelineLLM](https://huggingface.co/papers/2412.10423) (2024)\n* [Separate the Wheat from the Chaff: A Post-Hoc Approach to Safety Re-Alignment for Fine-Tuned Language Models](https://huggingface.co/papers/2412.11041) (2024)\n* [Computing Optimization-Based Prompt Injections Against Closed-Weights Models By Misusing a Fine-Tuning API](https://huggingface.co/papers/2501.09798) (2025)\n* [Siren: A Learning-Based Multi-Turn Attack Framework for Simulating Real-World Human Jailbreak Behaviors](https://huggingface.co/papers/2501.14250) (2025)\n* [SaLoRA: Safety-Alignment Preserved Low-Rank Adaptation](https://huggingface.co/papers/2501.01765) (2025)\n* [Backdoor Token Unlearning: Exposing and Defending Backdoors in Pretrained Language Models](https://huggingface.co/papers/2501.03272) (2025)\n* [Safeguarding Large Language Models in Real-time with Tunable Safety-Performance Trade-offs](https://huggingface.co/papers/2501.02018) (2025)\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/2501.17703.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2501.17703", "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* [RobustFT: Robust Supervised Fine-tuning for Large Language Models under Noisy Response](https://huggingface.co/papers/2412.14922) (2024)\n* [A Comparative Study of Learning Paradigms in Large Language Models via Intrinsic Dimension](https://huggingface.co/papers/2412.06245) (2024)\n* [Comparing Few-Shot Prompting of GPT-4 LLMs with BERT Classifiers for Open-Response Assessment in Tutor Equity Training](https://huggingface.co/papers/2501.06658) (2025)\n* [Boosting Tool Use of Large Language Models via Iterative Reinforced Fine-Tuning](https://huggingface.co/papers/2501.09766) (2025)\n* [Inference-Aware Fine-Tuning for Best-of-N Sampling in Large Language Models](https://huggingface.co/papers/2412.15287) (2024)\n* [From Drafts to Answers: Unlocking LLM Potential via Aggregation Fine-Tuning](https://huggingface.co/papers/2501.11877) (2025)\n* [Aligning Instruction Tuning with Pre-training](https://huggingface.co/papers/2501.09368) (2025)\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/2501.17749.json

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+{"paper_url": "https://huggingface.co/papers/2501.17749", "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* [ASTRAL: Automated Safety Testing of Large Language Models](https://huggingface.co/papers/2501.17132) (2025)\n* [MSTS: A Multimodal Safety Test Suite for Vision-Language Models](https://huggingface.co/papers/2501.10057) (2025)\n* [Agent-SafetyBench: Evaluating the Safety of LLM Agents](https://huggingface.co/papers/2412.14470) (2024)\n* [Fearless Unsafe. A More User-friendly Document for Unsafe Rust Programming Base on Refined Safety Properties](https://huggingface.co/papers/2412.06251) (2024)\n* [Fundamental Risks in the Current Deployment of General-Purpose AI Models: What Have We (Not) Learnt From Cybersecurity?](https://huggingface.co/papers/2501.01435) (2024)\n* [LLMs are Vulnerable to Malicious Prompts Disguised as Scientific Language](https://huggingface.co/papers/2501.14073) (2025)\n* [LLMs Lost in Translation: M-ALERT uncovers Cross-Linguistic Safety Gaps](https://huggingface.co/papers/2412.15035) (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`"}