{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/method/multi-head-attention/papers/59","list_of":"/method/multi-head-attention","method":"Multi-Head Attention","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":59,"pages_in_order":249,"rows_per_page":100,"rows":[5801,5900],"of":24855,"counts":{"archive_papers_tagged":24855,"with_a_code_link":11214,"where_syntology_ran_a_sample":3454,"not_listed_spam_title":0,"listed":24855,"listed_where_code_ran":3454,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":2916,"every_run_a_failure_of_syntologys_instrument":538,"listed_with_a_run_with_no_instrument_failure":2916,"listed_every_run_a_failure_of_syntologys_instrument":538,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/method/multi-head-attention","prev":"/method/multi-head-attention/papers/58","next":"/method/multi-head-attention/papers/60","papers":[{"paper":null,"slug":"fairberts-erasing-sensitive-information","title":"fairBERTs: Erasing Sensitive Information Through Semantic and Fairness-aware Perturbations","date":"2024-07-11","arxiv_id":"2407.08189","n_code_links":0,"syntology":null},{"paper":null,"slug":"fault-diagnosis-in-power-grids-with-large","title":"Fault Diagnosis in Power Grids with Large Language Model","date":"2024-07-11","arxiv_id":"2407.08836","n_code_links":0,"syntology":null},{"paper":"/paper/flashattention-3-fast-and-accurate-attention","slug":"flashattention-3-fast-and-accurate-attention","title":"FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision","date":"2024-07-11","arxiv_id":"2407.08608","n_code_links":2,"syntology":{"ran":16,"of":18,"n_ran_checked":15,"n_instrument":1,"unverified":2,"pointer_only":10,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 2 honoured, 0 violated, 13 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","official":{"repos":["dao-ailab/flash-attention"],"state":"official (archive's flag): 16 ran","n_ran":16,"n_constructed":0,"n_ran_no_instrument_failure":15,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"gpt-4-is-judged-more-human-than-humans-in","title":"GPT-4 is judged more human than humans in displaced and inverted Turing tests","date":"2024-07-11","arxiv_id":"2407.08853","n_code_links":0,"syntology":null},{"paper":"/paper/graphmamba-an-efficient-graph-structure","slug":"graphmamba-an-efficient-graph-structure","title":"GraphMamba: An Efficient Graph Structure Learning Vision Mamba for Hyperspectral Image Classification","date":"2024-07-11","arxiv_id":"2407.08255","n_code_links":1,"syntology":null},{"paper":"/paper/gta-a-benchmark-for-general-tool-agents","slug":"gta-a-benchmark-for-general-tool-agents","title":"GTA: A Benchmark for General Tool Agents","date":"2024-07-11","arxiv_id":"2407.08713","n_code_links":1,"syntology":null},{"paper":null,"slug":"hdt-hierarchical-document-transformer","title":"HDT: Hierarchical Document Transformer","date":"2024-07-11","arxiv_id":"2407.08330","n_code_links":0,"syntology":null},{"paper":null,"slug":"investigating-llms-as-voting-assistants-via","title":"Investigating LLMs as Voting Assistants via Contextual Augmentation: A Case Study on the European Parliament Elections 2024","date":"2024-07-11","arxiv_id":"2407.08495","n_code_links":0,"syntology":null},{"paper":"/paper/knowledge-distillation-to-effectively-attain","slug":"knowledge-distillation-to-effectively-attain","title":"Knowledge distillation to effectively attain both region-of-interest and global semantics from an image where multiple objects appear","date":"2024-07-11","arxiv_id":"2407.08257","n_code_links":1,"syntology":null},{"paper":"/paper/llms-morphological-analyses-of-complex-fst","slug":"llms-morphological-analyses-of-complex-fst","title":"LLMs' morphological analyses of complex FST-generated Finnish words","date":"2024-07-11","arxiv_id":"2407.08269","n_code_links":1,"syntology":null},{"paper":"/paper/mavis-mathematical-visual-instruction-tuning","slug":"mavis-mathematical-visual-instruction-tuning","title":"MAVIS: Mathematical Visual Instruction Tuning with an Automatic Data Engine","date":"2024-07-11","arxiv_id":"2407.08739","n_code_links":3,"syntology":null},{"paper":"/paper/multimodal-contrastive-learning-for-spatial","slug":"multimodal-contrastive-learning-for-spatial","title":"Multimodal contrastive learning for spatial gene expression prediction using histology images","date":"2024-07-11","arxiv_id":"2407.08216","n_code_links":1,"syntology":{"ran":8,"of":9,"n_ran_checked":7,"n_instrument":1,"unverified":1,"pointer_only":9,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["shizhiceng/mclstexp"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"on-the-in-security-of-llm-app-stores","title":"On the (In)Security of LLM App Stores","date":"2024-07-11","arxiv_id":"2407.08422","n_code_links":0,"syntology":null},{"paper":null,"slug":"performance-barrier-event-triggered-control","title":"Performance-Barrier Event-Triggered Control of a Class of Reaction-Diffusion PDEs","date":"2024-07-11","arxiv_id":"2407.08178","n_code_links":0,"syntology":null},{"paper":"/paper/projecting-points-to-axes-oriented-object","slug":"projecting-points-to-axes-oriented-object","title":"Projecting Points to Axes: Oriented Object Detection via Point-Axis Representation","date":"2024-07-11","arxiv_id":"2407.08489","n_code_links":1,"syntology":null},{"paper":null,"slug":"real-time-anomaly-detection-and-reactive","title":"Real-Time Anomaly Detection and Reactive Planning with Large Language Models","date":"2024-07-11","arxiv_id":"2407.08735","n_code_links":0,"syntology":null},{"paper":"/paper/salsa-swift-adaptive-lightweight-self","slug":"salsa-swift-adaptive-lightweight-self","title":"SALSA: Swift Adaptive Lightweight Self-Attention for Enhanced LiDAR Place Recognition","date":"2024-07-11","arxiv_id":"2407.08260","n_code_links":1,"syntology":null},{"paper":null,"slug":"skywork-math-data-scaling-laws-for","title":"Skywork-Math: Data Scaling Laws for Mathematical Reasoning in Large Language Models -- The Story Goes On","date":"2024-07-11","arxiv_id":"2407.08348","n_code_links":0,"syntology":null},{"paper":null,"slug":"speculative-rag-enhancing-retrieval-augmented","title":"Speculative RAG: Enhancing Retrieval Augmented Generation through Drafting","date":"2024-07-11","arxiv_id":"2407.08223","n_code_links":0,"syntology":null},{"paper":null,"slug":"spiking-tucker-fusion-transformer-for-audio","title":"Spiking Tucker Fusion Transformer for Audio-Visual Zero-Shot Learning","date":"2024-07-11","arxiv_id":"2407.08130","n_code_links":0,"syntology":null},{"paper":"/paper/stentrans-transformer-based-deep-learning-for","slug":"stentrans-transformer-based-deep-learning-for","title":"stEnTrans: Transformer-based deep learning for spatial transcriptomics enhancement","date":"2024-07-11","arxiv_id":"2407.08224","n_code_links":1,"syntology":null},{"paper":null,"slug":"synthetic-electroretinogram-signal-generation","title":"Synthetic Electroretinogram Signal Generation Using Conditional Generative Adversarial Network for Enhancing Classification of Autism Spectrum Disorder","date":"2024-07-11","arxiv_id":"2407.08166","n_code_links":0,"syntology":null},{"paper":null,"slug":"tractgraphformer-anatomically-informed-hybrid","title":"TractGraphFormer: Anatomically Informed Hybrid Graph CNN-Transformer Network for Classification from Diffusion MRI Tractography","date":"2024-07-11","arxiv_id":"2407.08883","n_code_links":0,"syntology":null},{"paper":"/paper/vox-populi-vox-ai-using-language-models-to","slug":"vox-populi-vox-ai-using-language-models-to","title":"Vox Populi, Vox AI? Using Language Models to Estimate German Public Opinion","date":"2024-07-11","arxiv_id":"2407.08563","n_code_links":1,"syntology":null},{"paper":"/paper/wildgaussians-3d-gaussian-splatting-in-the","slug":"wildgaussians-3d-gaussian-splatting-in-the","title":"WildGaussians: 3D Gaussian Splatting in the Wild","date":"2024-07-11","arxiv_id":"2407.08447","n_code_links":1,"syntology":{"ran":19,"of":21,"n_ran_checked":16,"n_instrument":3,"unverified":2,"pointer_only":21,"phrase":"19 ran (of which 0 constructed an object rather than computing a result; 16 with no instrument failure: 2 honoured, 0 violated, 14 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","official":{"repos":["jkulhanek/wild-gaussians"],"state":"official (archive's flag): 19 ran","n_ran":19,"n_constructed":0,"n_ran_no_instrument_failure":16,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/arabic-automatic-story-generation-with-large","slug":"arabic-automatic-story-generation-with-large","title":"Arabic Automatic Story Generation with Large Language Models","date":"2024-07-10","arxiv_id":"2407.07551","n_code_links":1,"syntology":null},{"paper":"/paper/attribute-or-abstain-large-language-models-as","slug":"attribute-or-abstain-large-language-models-as","title":"Attribute or Abstain: Large Language Models as Long Document Assistants","date":"2024-07-10","arxiv_id":"2407.07799","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["ukplab/arxiv2024-attribute-or-abstain"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/deep-er-reconstruction-of-imaging-cherenkov","slug":"deep-er-reconstruction-of-imaging-cherenkov","title":"Deep(er) Reconstruction of Imaging Cherenkov Detectors with Swin Transformers and Normalizing Flow Models","date":"2024-07-10","arxiv_id":"2407.07376","n_code_links":1,"syntology":null},{"paper":"/paper/ds-gt-erisk-2024-sentence-transformers-for","slug":"ds-gt-erisk-2024-sentence-transformers-for","title":"DS@GT eRisk 2024: Sentence Transformers for Social Media Risk Assessment","date":"2024-07-10","arxiv_id":"2407.08008","n_code_links":1,"syntology":null},{"paper":"/paper/evaluating-large-language-models-with-grid","slug":"evaluating-large-language-models-with-grid","title":"Evaluating Large Language Models with Grid-Based Game Competitions: An Extensible LLM Benchmark and Leaderboard","date":"2024-07-10","arxiv_id":"2407.07796","n_code_links":1,"syntology":null},{"paper":null,"slug":"facts-about-building-retrieval-augmented","title":"FACTS About Building Retrieval Augmented Generation-based Chatbots","date":"2024-07-10","arxiv_id":"2407.07858","n_code_links":0,"syntology":null},{"paper":"/paper/federated-foundation-model-for-cardiac-ct","slug":"federated-foundation-model-for-cardiac-ct","title":"Real World Federated Learning with a Knowledge Distilled Transformer for Cardiac CT Imaging","date":"2024-07-10","arxiv_id":"2407.07557","n_code_links":2,"syntology":null},{"paper":"/paper/fsponer-few-shot-prompt-optimization-for","slug":"fsponer-few-shot-prompt-optimization-for","title":"FsPONER: Few-shot Prompt Optimization for Named Entity Recognition in Domain-specific Scenarios","date":"2024-07-10","arxiv_id":"2407.08035","n_code_links":1,"syntology":null},{"paper":"/paper/h-fcbformer-hierarchical-fully-convolutional","slug":"h-fcbformer-hierarchical-fully-convolutional","title":"H-FCBFormer Hierarchical Fully Convolutional Branch Transformer for Occlusal Contact Segmentation with Articulating Paper","date":"2024-07-10","arxiv_id":"2407.07604","n_code_links":1,"syntology":null},{"paper":null,"slug":"haformer-unleashing-the-power-of-hierarchy","title":"HAFormer: Unleashing the Power of Hierarchy-Aware Features for Lightweight Semantic Segmentation","date":"2024-07-10","arxiv_id":"2407.07441","n_code_links":0,"syntology":null},{"paper":"/paper/kpopmt-translation-dataset-with-terminology","slug":"kpopmt-translation-dataset-with-terminology","title":"KpopMT: Translation Dataset with Terminology for Kpop Fandom","date":"2024-07-10","arxiv_id":"2407.07413","n_code_links":1,"syntology":null},{"paper":null,"slug":"large-language-model-augmented-auto","title":"Large Language Model-Augmented Auto-Delineation of Treatment Target Volume in Radiation Therapy","date":"2024-07-10","arxiv_id":"2407.07296","n_code_links":0,"syntology":null},{"paper":"/paper/litsearch-a-retrieval-benchmark-for","slug":"litsearch-a-retrieval-benchmark-for","title":"LitSearch: A Retrieval Benchmark for Scientific Literature Search","date":"2024-07-10","arxiv_id":"2407.18940","n_code_links":1,"syntology":{"ran":7,"of":7,"n_ran_checked":7,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["princeton-nlp/litsearch"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"mixsumm-topic-based-data-augmentation-using","title":"A Guide To Effectively Leveraging LLMs for Low-Resource Text Summarization: Data Augmentation and Semi-supervised Approaches","date":"2024-07-10","arxiv_id":"2407.07341","n_code_links":0,"syntology":null},{"paper":null,"slug":"multilingual-blending-llm-safety-alignment","title":"Multilingual Blending: LLM Safety Alignment Evaluation with Language Mixture","date":"2024-07-10","arxiv_id":"2407.07342","n_code_links":0,"syntology":null},{"paper":null,"slug":"probability-of-differentiation-reveals","title":"Probability of Differentiation Reveals Brittleness of Homogeneity Bias in GPT-4","date":"2024-07-10","arxiv_id":"2407.07329","n_code_links":0,"syntology":null},{"paper":null,"slug":"rag-vs-long-context-examining-frontier-large","title":"Examining Long-Context Large Language Models for Environmental Review Document Comprehension","date":"2024-07-10","arxiv_id":"2407.07321","n_code_links":0,"syntology":null},{"paper":"/paper/rosa-random-subspace-adaptation-for-efficient","slug":"rosa-random-subspace-adaptation-for-efficient","title":"ROSA: Random Subspace Adaptation for Efficient Fine-Tuning","date":"2024-07-10","arxiv_id":"2407.07802","n_code_links":1,"syntology":null},{"paper":null,"slug":"rt-la-voce-real-time-low-snr-audio-visual","title":"RT-LA-VocE: Real-Time Low-SNR Audio-Visual Speech Enhancement","date":"2024-07-10","arxiv_id":"2407.07825","n_code_links":0,"syntology":null},{"paper":"/paper/swin-smt-global-sequential-modeling-in-3d","slug":"swin-smt-global-sequential-modeling-in-3d","title":"Swin SMT: Global Sequential Modeling in 3D Medical Image Segmentation","date":"2024-07-10","arxiv_id":"2407.07514","n_code_links":1,"syntology":null},{"paper":"/paper/swiss-dino-efficient-and-versatile-vision","slug":"swiss-dino-efficient-and-versatile-vision","title":"Swiss DINO: Efficient and Versatile Vision Framework for On-device Personal Object Search","date":"2024-07-10","arxiv_id":"2407.07541","n_code_links":1,"syntology":null},{"paper":null,"slug":"teaching-transformers-causal-reasoning","title":"Teaching Transformers Causal Reasoning through Axiomatic Training","date":"2024-07-10","arxiv_id":"2407.07612","n_code_links":0,"syntology":null},{"paper":null,"slug":"toto-time-series-optimized-transformer-for","title":"Toto: Time Series Optimized Transformer for Observability","date":"2024-07-10","arxiv_id":"2407.07874","n_code_links":0,"syntology":null},{"paper":null,"slug":"video-in-context-learning","title":"Video In-context Learning","date":"2024-07-10","arxiv_id":"2407.07356","n_code_links":0,"syntology":null},{"paper":null,"slug":"when-to-accept-automated-predictions-and-when","title":"When to Accept Automated Predictions and When to Defer to Human Judgment?","date":"2024-07-10","arxiv_id":"2407.07821","n_code_links":0,"syntology":null},{"paper":null,"slug":"worldapis-the-world-is-worth-how-many-apis-a","title":"WorldAPIs: The World Is Worth How Many APIs? A Thought Experiment","date":"2024-07-10","arxiv_id":"2407.07778","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-predictive-model-based-on-transformer-with","title":"A Predictive Model Based on Transformer with Statistical Feature Embedding in Manufacturing Sensor Dataset","date":"2024-07-09","arxiv_id":"2407.06682","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-simple-architecture-for-enterprise-large","title":"A Simple Architecture for Enterprise Large Language Model Applications based on Role based security and Clearance Levels using Retrieval-Augmented Generation or Mixture of Experts","date":"2024-07-09","arxiv_id":"2407.06718","n_code_links":0,"syntology":null},{"paper":"/paper/ai-ai-bias-large-language-models-favor-their","slug":"ai-ai-bias-large-language-models-favor-their","title":"AI AI Bias: Large Language Models Favor Their Own Generated Content","date":"2024-07-09","arxiv_id":"2407.12856","n_code_links":1,"syntology":null},{"paper":"/paper/automated-peer-reviewing-in-paper-sea","slug":"automated-peer-reviewing-in-paper-sea","title":"Automated Peer Reviewing in Paper SEA: Standardization, Evaluation, and Analysis","date":"2024-07-09","arxiv_id":"2407.12857","n_code_links":1,"syntology":{"ran":7,"of":14,"n_ran_checked":7,"n_instrument":0,"unverified":7,"pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 7 unverified","official":{"repos":["ecnu-sea/SEA"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":7,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"capformer-compression-aware-pre-trained","title":"CAPformer: Compression-Aware Pre-trained Transformer for Low-Light Image Enhancement","date":"2024-07-09","arxiv_id":"2407.07056","n_code_links":0,"syntology":null},{"paper":"/paper/chatgpt-doesn-t-trust-chargers-fans-guardrail","slug":"chatgpt-doesn-t-trust-chargers-fans-guardrail","title":"ChatGPT Doesn't Trust Chargers Fans: Guardrail Sensitivity in Context","date":"2024-07-09","arxiv_id":"2407.06866","n_code_links":1,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"0 ran · 1 unverified","official":{"repos":["vli31/llm-guardrail-sensitivity"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"paper":null,"slug":"convnlp-image-based-ai-text-detection","title":"ConvNLP: Image-based AI Text Detection","date":"2024-07-09","arxiv_id":"2407.07225","n_code_links":0,"syntology":null},{"paper":null,"slug":"cormult-a-semi-supervised-modality","title":"CorMulT: A Semi-supervised Modality Correlation-aware Multimodal Transformer for Sentiment Analysis","date":"2024-07-09","arxiv_id":"2407.07046","n_code_links":0,"syntology":null},{"paper":null,"slug":"empirical-analysis-of-biding-precedent","title":"Empirical analysis of Binding Precedent efficiency in the Brazilian Supreme Court via Similar Case Retrieval","date":"2024-07-09","arxiv_id":"2407.07004","n_code_links":0,"syntology":null},{"paper":"/paper/fine-tuning-linear-layers-only-is-a-simple","slug":"fine-tuning-linear-layers-only-is-a-simple","title":"Fine-Tuning Attention Modules Only: Enhancing Weight Disentanglement in Task Arithmetic","date":"2024-07-09","arxiv_id":"2407.07089","n_code_links":2,"syntology":{"ran":3,"of":5,"n_ran_checked":0,"n_instrument":3,"unverified":2,"pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","official":{"repos":["kyrie-23/task_arithmetic_tangent","kyrie-23/linear_task_arithmetic"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"identification-of-emotions-on-twitter-during","title":"Identification of emotions on Twitter during the 2022 electoral process in Colombia","date":"2024-07-09","arxiv_id":"2407.07258","n_code_links":0,"syntology":null},{"paper":null,"slug":"measuring-sustainability-intention-of-esg","title":"Measuring Sustainability Intention of ESG Fund Disclosure using Few-Shot Learning","date":"2024-07-09","arxiv_id":"2407.06893","n_code_links":0,"syntology":null},{"paper":null,"slug":"mixture-of-modules-reinventing-transformers","title":"Mixture-of-Modules: Reinventing Transformers as Dynamic Assemblies of Modules","date":"2024-07-09","arxiv_id":"2407.06677","n_code_links":0,"syntology":null},{"paper":"/paper/parameter-efficient-and-memory-efficient","slug":"parameter-efficient-and-memory-efficient","title":"Parameter-Efficient and Memory-Efficient Tuning for Vision Transformer: A Disentangled Approach","date":"2024-07-09","arxiv_id":"2407.06964","n_code_links":1,"syntology":null},{"paper":"/paper/peer-expertizing-domain-specific-tasks-with-a","slug":"peer-expertizing-domain-specific-tasks-with-a","title":"PEER: Expertizing Domain-Specific Tasks with a Multi-Agent Framework and Tuning Methods","date":"2024-07-09","arxiv_id":"2407.06985","n_code_links":1,"syntology":null},{"paper":null,"slug":"prompting-techniques-for-secure-code","title":"Prompting Techniques for Secure Code Generation: A Systematic Investigation","date":"2024-07-09","arxiv_id":"2407.07064","n_code_links":0,"syntology":null},{"paper":null,"slug":"raply-a-profanity-mitigated-rap-generator","title":"Raply: A profanity-mitigated rap generator","date":"2024-07-09","arxiv_id":"2407.06941","n_code_links":0,"syntology":null},{"paper":null,"slug":"segment-based-interactive-machine-translation","title":"Segment-Based Interactive Machine Translation for Pre-trained Models","date":"2024-07-09","arxiv_id":"2407.06990","n_code_links":0,"syntology":null},{"paper":null,"slug":"solving-general-natural-language-description","title":"Solving General Natural-Language-Description Optimization Problems with Large Language Models","date":"2024-07-09","arxiv_id":"2407.07924","n_code_links":0,"syntology":null},{"paper":"/paper/source-code-summarization-in-the-era-of-large","slug":"source-code-summarization-in-the-era-of-large","title":"Source Code Summarization in the Era of Large Language Models","date":"2024-07-09","arxiv_id":"2407.07959","n_code_links":1,"syntology":null},{"paper":"/paper/spanish-trocr-leveraging-transfer-learning","slug":"spanish-trocr-leveraging-transfer-learning","title":"Spanish TrOCR: Leveraging Transfer Learning for Language Adaptation","date":"2024-07-09","arxiv_id":"2407.06950","n_code_links":1,"syntology":null},{"paper":"/paper/trackformers-in-search-of-transformer-based","slug":"trackformers-in-search-of-transformer-based","title":"TrackFormers: In Search of Transformer-Based Particle Tracking for the High-Luminosity LHC Era","date":"2024-07-09","arxiv_id":"2407.07179","n_code_links":1,"syntology":null},{"paper":null,"slug":"using-large-language-models-for-generating","title":"Using Large Language Models for Generating Smart Contracts for Health Insurance from Textual Policies","date":"2024-07-09","arxiv_id":"2407.07019","n_code_links":0,"syntology":null},{"paper":null,"slug":"using-pretrained-large-language-model-with","title":"Using Pretrained Large Language Model with Prompt Engineering to Answer Biomedical Questions","date":"2024-07-09","arxiv_id":"2407.06779","n_code_links":0,"syntology":null},{"paper":"/paper/3d-vision-and-language-pretraining-with-large","slug":"3d-vision-and-language-pretraining-with-large","title":"3D Vision and Language Pretraining with Large-Scale Synthetic Data","date":"2024-07-08","arxiv_id":"2407.06084","n_code_links":1,"syntology":{"ran":10,"of":10,"n_ran_checked":9,"n_instrument":1,"unverified":0,"pointer_only":1,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["idejie/3DSyn"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/a-single-transformer-for-scalable-vision","slug":"a-single-transformer-for-scalable-vision","title":"SOLO: A Single Transformer for Scalable Vision-Language Modeling","date":"2024-07-08","arxiv_id":"2407.06438","n_code_links":1,"syntology":{"ran":2,"of":3,"n_ran_checked":2,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["yangyi-chen/solo"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/an-empirical-comparison-of-vocabulary","slug":"an-empirical-comparison-of-vocabulary","title":"An Empirical Comparison of Vocabulary Expansion and Initialization Approaches for Language Models","date":"2024-07-08","arxiv_id":"2407.05841","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["AI4Bharat/VocabAdaptation_LLM"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"charss-character-level-transformer-model-for","title":"CharSS: Character-Level Transformer Model for Sanskrit Word Segmentation","date":"2024-07-08","arxiv_id":"2407.06331","n_code_links":0,"syntology":null},{"paper":"/paper/codeupdatearena-benchmarking-knowledge","slug":"codeupdatearena-benchmarking-knowledge","title":"CodeUpdateArena: Benchmarking Knowledge Editing on API Updates","date":"2024-07-08","arxiv_id":"2407.06249","n_code_links":1,"syntology":null},{"paper":null,"slug":"cross-domain-few-shot-in-context-learning-for","title":"Cross-domain Few-shot In-context Learning for Enhancing Traffic Sign Recognition","date":"2024-07-08","arxiv_id":"2407.05814","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-learning-based-anomaly-detection-and-log","title":"Deep Learning-based Anomaly Detection and Log Analysis for Computer Networks","date":"2024-07-08","arxiv_id":"2407.05639","n_code_links":0,"syntology":null},{"paper":"/paper/empowering-1000-tokens-second-on-device-llm","slug":"empowering-1000-tokens-second-on-device-llm","title":"Fast On-device LLM Inference with NPUs","date":"2024-07-08","arxiv_id":"2407.05858","n_code_links":1,"syntology":{"ran":7,"of":8,"n_ran_checked":7,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["ubiquitouslearning/mllm"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"generative-debunking-of-climate","title":"Generative Debunking of Climate Misinformation","date":"2024-07-08","arxiv_id":"2407.05599","n_code_links":0,"syntology":null},{"paper":"/paper/inversecoder-unleashing-the-power-of","slug":"inversecoder-unleashing-the-power-of","title":"InverseCoder: Self-improving Instruction-Tuned Code LLMs with Inverse-Instruct","date":"2024-07-08","arxiv_id":"2407.05700","n_code_links":1,"syntology":{"ran":11,"of":11,"n_ran_checked":11,"n_instrument":0,"unverified":0,"pointer_only":11,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["wyt2000/InverseCoder"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/large-language-models-understand-layouts","slug":"large-language-models-understand-layouts","title":"Large Language Models Understand Layout","date":"2024-07-08","arxiv_id":"2407.05750","n_code_links":1,"syntology":null},{"paper":null,"slug":"learning-lane-graphs-from-aerial-imagery","title":"Learning Lane Graphs from Aerial Imagery Using Transformers","date":"2024-07-08","arxiv_id":"2407.05687","n_code_links":0,"syntology":null},{"paper":"/paper/leveraging-transformers-for-weakly-supervised","slug":"leveraging-transformers-for-weakly-supervised","title":"Leveraging Transformers for Weakly Supervised Object Localization in Unconstrained Videos","date":"2024-07-08","arxiv_id":"2407.06018","n_code_links":1,"syntology":null},{"paper":"/paper/llm-based-open-domain-integrated-task-and","slug":"llm-based-open-domain-integrated-task-and","title":"Controllable and Reliable Knowledge-Intensive Task-Oriented Conversational Agents with Declarative Genie Worksheets","date":"2024-07-08","arxiv_id":"2407.05674","n_code_links":1,"syntology":null},{"paper":"/paper/meme-analysis-using-llm-based-contextual","slug":"meme-analysis-using-llm-based-contextual","title":"Meme Analysis using LLM-based Contextual Information and U-net Encapsulated Transformer","date":"2024-07-08","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"mstf-multiscale-transformer-for-incomplete","title":"MSTF: Multiscale Transformer for Incomplete Trajectory Prediction","date":"2024-07-08","arxiv_id":"2407.05671","n_code_links":0,"syntology":null},{"paper":"/paper/multi-label-plant-species-classification-with","slug":"multi-label-plant-species-classification-with","title":"Multi-Label Plant Species Classification with Self-Supervised Vision Transformers","date":"2024-07-08","arxiv_id":"2407.06298","n_code_links":1,"syntology":null},{"paper":null,"slug":"on-the-power-of-convolution-augmented","title":"On the Power of Convolution Augmented Transformer","date":"2024-07-08","arxiv_id":"2407.05591","n_code_links":0,"syntology":null},{"paper":null,"slug":"personality-analysis-for-social-media-users","title":"Personality Analysis for Social Media Users using Arabic language and its Effect on Sentiment Analysis","date":"2024-07-08","arxiv_id":"2407.06314","n_code_links":0,"syntology":null},{"paper":null,"slug":"potential-of-multimodal-large-language-models","title":"Potential of Multimodal Large Language Models for Data Mining of Medical Images and Free-text Reports","date":"2024-07-08","arxiv_id":"2407.05758","n_code_links":0,"syntology":null},{"paper":"/paper/pruning-large-language-models-to-intra-module","slug":"pruning-large-language-models-to-intra-module","title":"Pruning Large Language Models to Intra-module Low-rank Architecture with Transitional Activations","date":"2024-07-08","arxiv_id":"2407.05690","n_code_links":1,"syntology":null},{"paper":null,"slug":"stmr-spiral-transformer-for-hand-mesh","title":"STMR: Spiral Transformer for Hand Mesh Reconstruction","date":"2024-07-08","arxiv_id":"2407.05967","n_code_links":0,"syntology":null},{"paper":null,"slug":"surprising-gender-biases-in-gpt","title":"Surprising gender biases in GPT","date":"2024-07-08","arxiv_id":"2407.06003","n_code_links":0,"syntology":null},{"paper":null,"slug":"swin-unetr-segmentation-with-automated","title":"Swin UNETR segmentation with automated geometry filtering for biomechanical modeling of knee joint cartilage","date":"2024-07-08","arxiv_id":"2407.06403","n_code_links":0,"syntology":null},{"paper":null,"slug":"t2vsafetybench-evaluating-the-safety-of-text","title":"T2VSafetyBench: Evaluating the Safety of Text-to-Video Generative Models","date":"2024-07-08","arxiv_id":"2407.05965","n_code_links":0,"syntology":null}],"record_sha256":"bb4633d2017ec9460e1053c15cd771f6d6ab2a306ba9c8f3ddd523081811e1b1","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}