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data/2406.07057.json

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+{"paper_url": "https://huggingface.co/papers/2406.07057", "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* [II-Bench: An Image Implication Understanding Benchmark for Multimodal Large Language Models](https://huggingface.co/papers/2406.05862) (2024)\n* [EmoLLM: Multimodal Emotional Understanding Meets Large Language Models](https://huggingface.co/papers/2406.16442) (2024)\n* [MLLMGuard: A Multi-dimensional Safety Evaluation Suite for Multimodal Large Language Models](https://huggingface.co/papers/2406.07594) (2024)\n* [Cross-Modality Jailbreak and Mismatched Attacks on Medical Multimodal Large Language Models](https://huggingface.co/papers/2405.20775) (2024)\n* [CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs](https://huggingface.co/papers/2406.18521) (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/2406.13897.json

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+{"paper_url": "https://huggingface.co/papers/2406.13897", "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* [CraftsMan: High-fidelity Mesh Generation with 3D Native Generation and Interactive Geometry Refiner](https://huggingface.co/papers/2405.14979) (2024)\n* [Direct3D: Scalable Image-to-3D Generation via 3D Latent Diffusion Transformer](https://huggingface.co/papers/2405.14832) (2024)\n* [LDM: Large Tensorial SDF Model for Textured Mesh Generation](https://huggingface.co/papers/2405.14580) (2024)\n* [ID-to-3D: Expressive ID-guided 3D Heads via Score Distillation Sampling](https://huggingface.co/papers/2405.16570) (2024)\n* [StableMaterials: Enhancing Diversity in Material Generation via Semi-Supervised Learning](https://huggingface.co/papers/2406.09293) (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/2407.10424.json

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+{"paper_url": "https://huggingface.co/papers/2407.10424", "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* [InverseCoder: Unleashing the Power of Instruction-Tuned Code LLMs with Inverse-Instruct](https://huggingface.co/papers/2407.05700) (2024)\n* [Towards LLM-Powered Verilog RTL Assistant: Self-Verification and Self-Correction](https://huggingface.co/papers/2406.00115) (2024)\n* [AutoCoder: Enhancing Code Large Language Model with AIEV-Instruct](https://huggingface.co/papers/2405.14906) (2024)\n* [MG-Verilog: Multi-grained Dataset Towards Enhanced LLM-assisted Verilog Generation](https://huggingface.co/papers/2407.01910) (2024)\n* [ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation](https://huggingface.co/papers/2405.17057) (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/2407.12854.json

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+{"paper_url": "https://huggingface.co/papers/2407.12854", "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* [RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMs](https://huggingface.co/papers/2407.02485) (2024)\n* [Large Language Model-guided Document Selection](https://huggingface.co/papers/2406.04638) (2024)\n* [Zyda: A 1.3T Dataset for Open Language Modeling](https://huggingface.co/papers/2406.01981) (2024)\n* [Neurocache: Efficient Vector Retrieval for Long-range Language Modeling](https://huggingface.co/papers/2407.02486) (2024)\n* [LongRAG: Enhancing Retrieval-Augmented Generation with Long-context LLMs](https://huggingface.co/papers/2406.15319) (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/2407.12883.json

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+{"paper_url": "https://huggingface.co/papers/2407.12883", "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* [MR-BEN: A Comprehensive Meta-Reasoning Benchmark for Large Language Models](https://huggingface.co/papers/2406.13975) (2024)\n* [Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More?](https://huggingface.co/papers/2406.13121) (2024)\n* [DEXTER: A Benchmark for open-domain Complex Question Answering using LLMs](https://huggingface.co/papers/2406.17158) (2024)\n* [A Multi-Source Retrieval Question Answering Framework Based on RAG](https://huggingface.co/papers/2405.19207) (2024)\n* [Enhancing Knowledge Retrieval with In-Context Learning and Semantic Search through Generative AI](https://huggingface.co/papers/2406.09621) (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/2407.12982.json

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+{"paper_url": "https://huggingface.co/papers/2407.12982", "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 Model Enhanced Knowledge Representation Learning: A Survey](https://huggingface.co/papers/2407.00936) (2024)\n* [Enhancing Knowledge Retrieval with In-Context Learning and Semantic Search through Generative AI](https://huggingface.co/papers/2406.09621) (2024)\n* [A Multi-Source Retrieval Question Answering Framework Based on RAG](https://huggingface.co/papers/2405.19207) (2024)\n* [Retrieval Meets Reasoning: Even High-school Textbook Knowledge Benefits Multimodal Reasoning](https://huggingface.co/papers/2405.20834) (2024)\n* [A Survey of Generative Information Retrieval](https://huggingface.co/papers/2406.01197) (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/2407.13244.json

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+{"paper_url": "https://huggingface.co/papers/2407.13244", "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* [Evaluating the Ability of LLMs to Solve Semantics-Aware Process Mining Tasks](https://huggingface.co/papers/2407.02310) (2024)\n* [LogEval: A Comprehensive Benchmark Suite for Large Language Models In Log Analysis](https://huggingface.co/papers/2407.01896) (2024)\n* [Towards a Benchmark for Causal Business Process Reasoning with LLMs](https://huggingface.co/papers/2406.05506) (2024)\n* [Benchmarking Open-Source Language Models for Efficient Question Answering in Industrial Applications](https://huggingface.co/papers/2406.13713) (2024)\n* [DABL: Detecting Semantic Anomalies in Business Processes Using Large Language Models](https://huggingface.co/papers/2406.15781) (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/2407.13481.json

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+{"paper_url": "https://huggingface.co/papers/2407.13481", "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* [RecGPT: Generative Pre-training for Text-based Recommendation](https://huggingface.co/papers/2405.12715) (2024)\n* [Taxonomy-Guided Zero-Shot Recommendations with LLMs](https://huggingface.co/papers/2406.14043) (2024)\n* [AdaptEval: Evaluating Large Language Models on Domain Adaptation for Text Summarization](https://huggingface.co/papers/2407.11591) (2024)\n* [Item-Language Model for Conversational Recommendation](https://huggingface.co/papers/2406.02844) (2024)\n* [Learning to Reduce: Towards Improving Performance of Large Language Models on Structured Data](https://huggingface.co/papers/2407.02750) (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/2407.13623.json

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+{"paper_url": "https://huggingface.co/papers/2407.13623", "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* [Are Protein Language Models Compute Optimal?](https://huggingface.co/papers/2406.07249) (2024)\n* [Resolving Discrepancies in Compute-Optimal Scaling of Language Models](https://huggingface.co/papers/2406.19146) (2024)\n* [Scaling Laws for Linear Complexity Language Models](https://huggingface.co/papers/2406.16690) (2024)\n* [Large Vocabulary Size Improves Large Language Models](https://huggingface.co/papers/2406.16508) (2024)\n* [Repurposing Language Models into Embedding Models: Finding the Compute-Optimal Recipe](https://huggingface.co/papers/2406.04165) (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/2407.13638.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2407.13638", "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* [SNOBERT: A Benchmark for clinical notes entity linking in the SNOMED CT clinical terminology](https://huggingface.co/papers/2405.16115) (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/2407.13696.json

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+{"paper_url": "https://huggingface.co/papers/2407.13696", "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* [Quantifying Variance in Evaluation Benchmarks](https://huggingface.co/papers/2406.10229) (2024)\n* [From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline](https://huggingface.co/papers/2406.11939) (2024)\n* [A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendations](https://huggingface.co/papers/2407.04069) (2024)\n* [Lessons from the Trenches on Reproducible Evaluation of Language Models](https://huggingface.co/papers/2405.14782) (2024)\n* [Judging the Judges: Evaluating Alignment and Vulnerabilities in LLMs-as-Judges](https://huggingface.co/papers/2406.12624) (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/2407.13709.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2407.13709", "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* [Triple Preference Optimization: Achieving Better Alignment with Less Data in a Single Step Optimization](https://huggingface.co/papers/2405.16681) (2024)\n* [LIONs: An Empirically Optimized Approach to Align Language Models](https://huggingface.co/papers/2407.06542) (2024)\n* [WPO: Enhancing RLHF with Weighted Preference Optimization](https://huggingface.co/papers/2406.11827) (2024)\n* [SimPO: Simple Preference Optimization with a Reference-Free Reward](https://huggingface.co/papers/2405.14734) (2024)\n* [Multi-Reference Preference Optimization for Large Language Models](https://huggingface.co/papers/2405.16388) (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/2407.13739.json

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+{"paper_url": "https://huggingface.co/papers/2407.13739", "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* [RepoQA: Evaluating Long Context Code Understanding](https://huggingface.co/papers/2406.06025) (2024)\n* [LongSkywork: A Training Recipe for Efficiently Extending Context Length in Large Language Models](https://huggingface.co/papers/2406.00605) (2024)\n* [KV Cache Compression, But What Must We Give in Return? A Comprehensive Benchmark of Long Context Capable Approaches](https://huggingface.co/papers/2407.01527) (2024)\n* [Hierarchical Context Pruning: Optimizing Real-World Code Completion with Repository-Level Pretrained Code LLMs](https://huggingface.co/papers/2406.18294) (2024)\n* [Code Less, Align More: Efficient LLM Fine-tuning for Code Generation with Data Pruning](https://huggingface.co/papers/2407.05040) (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/2407.13759.json

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+{"paper_url": "https://huggingface.co/papers/2407.13759", "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* [MultiDiff: Consistent Novel View Synthesis from a Single Image](https://huggingface.co/papers/2406.18524) (2024)\n* [Director3D: Real-world Camera Trajectory and 3D Scene Generation from Text](https://huggingface.co/papers/2406.17601) (2024)\n* [Collaborative Video Diffusion: Consistent Multi-video Generation with Camera Control](https://huggingface.co/papers/2405.17414) (2024)\n* [4Diffusion: Multi-view Video Diffusion Model for 4D Generation](https://huggingface.co/papers/2405.20674) (2024)\n* [Vivid-ZOO: Multi-View Video Generation with Diffusion Model](https://huggingface.co/papers/2406.08659) (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/2407.13764.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2407.13764", "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* [VDG: Vision-Only Dynamic Gaussian for Driving Simulation](https://huggingface.co/papers/2406.18198) (2024)\n* [Dynamic Gaussian Marbles for Novel View Synthesis of Casual Monocular Videos](https://huggingface.co/papers/2406.18717) (2024)\n* [MoDGS: Dynamic Gaussian Splatting from Causually-captured Monocular Videos](https://huggingface.co/papers/2406.00434) (2024)\n* [MoSca: Dynamic Gaussian Fusion from Casual Videos via 4D Motion Scaffolds](https://huggingface.co/papers/2405.17421) (2024)\n* [Splatter a Video: Video Gaussian Representation for Versatile Processing](https://huggingface.co/papers/2406.13870) (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`"}