{"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/dense-connections/papers/93","list_of":"/method/dense-connections","method":"Dense Connections","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":93,"pages_in_order":293,"rows_per_page":100,"rows":[9201,9300],"of":29230,"counts":{"archive_papers_tagged":29230,"with_a_code_link":12972,"where_syntology_ran_a_sample":3929,"not_listed_spam_title":0,"listed":29230,"listed_where_code_ran":3929,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":3303,"every_run_a_failure_of_syntologys_instrument":626,"listed_with_a_run_with_no_instrument_failure":3303,"listed_every_run_a_failure_of_syntologys_instrument":626,"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/dense-connections","prev":"/method/dense-connections/papers/92","next":"/method/dense-connections/papers/94","papers":[{"paper":"/paper/reading-subtext-evaluating-large-language","slug":"reading-subtext-evaluating-large-language","title":"Reading Subtext: Evaluating Large Language Models on Short Story Summarization with Writers","date":"2024-03-02","arxiv_id":"2403.01061","n_code_links":2,"syntology":null},{"paper":null,"slug":"vbart-the-turkish-llm","title":"VBART: The Turkish LLM","date":"2024-03-02","arxiv_id":"2403.01308","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-systematic-evaluation-of-large-language-2","title":"Comparing large language models and human programmers for generating programming code","date":"2024-03-01","arxiv_id":"2403.00894","n_code_links":0,"syntology":null},{"paper":null,"slug":"atp-enabling-fast-llm-serving-via-attention","title":"ATP: Enabling Fast LLM Serving via Attention on Top Principal Keys","date":"2024-03-01","arxiv_id":"2403.02352","n_code_links":0,"syntology":null},{"paper":null,"slug":"crimson-empowering-strategic-reasoning-in","title":"Crimson: Empowering Strategic Reasoning in Cybersecurity through Large Language Models","date":"2024-03-01","arxiv_id":"2403.00878","n_code_links":0,"syntology":null},{"paper":"/paper/dams-detr-dynamic-adaptive-multispectral","slug":"dams-detr-dynamic-adaptive-multispectral","title":"DAMSDet: Dynamic Adaptive Multispectral Detection Transformer with Competitive Query Selection and Adaptive Feature Fusion","date":"2024-03-01","arxiv_id":"2403.00326","n_code_links":2,"syntology":{"ran":9,"of":11,"n_ran_checked":9,"n_instrument":0,"unverified":2,"pointer_only":1,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["gjj45/dams-detr","gjj45/damsdet"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/deformable-one-shot-face-stylization-via-dino","slug":"deformable-one-shot-face-stylization-via-dino","title":"Deformable One-shot Face Stylization via DINO Semantic Guidance","date":"2024-03-01","arxiv_id":"2403.00459","n_code_links":1,"syntology":null},{"paper":null,"slug":"dfin-sql-integrating-focused-schema-with-din","title":"DFIN-SQL: Integrating Focused Schema with DIN-SQL for Superior Accuracy in Large-Scale Databases","date":"2024-03-01","arxiv_id":"2403.00872","n_code_links":0,"syntology":null},{"paper":"/paper/dual-domain-strip-attention-for-image","slug":"dual-domain-strip-attention-for-image","title":"Dual-domain strip attention for image restoration","date":"2024-03-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"efficient-adapter-tuning-of-pre-trained","title":"Efficient Adapter Tuning of Pre-trained Speech Models for Automatic Speaker Verification","date":"2024-03-01","arxiv_id":"2403.00293","n_code_links":0,"syntology":null},{"paper":null,"slug":"event-triggered-robust-cooperative-output","title":"Event-Triggered Robust Cooperative Output Regulation for a Class of Linear Multi-Agent Systems with an Unknown Exosystem","date":"2024-03-01","arxiv_id":"2403.00645","n_code_links":0,"syntology":null},{"paper":null,"slug":"gender-bias-in-large-language-models-across","title":"Gender Bias in Large Language Models across Multiple Languages","date":"2024-03-01","arxiv_id":"2403.00277","n_code_links":0,"syntology":null},{"paper":null,"slug":"large-language-models-for-simultaneous-named","title":"Large Language Models for Simultaneous Named Entity Extraction and Spelling Correction","date":"2024-03-01","arxiv_id":"2403.00528","n_code_links":0,"syntology":null},{"paper":"/paper/merging-text-transformer-models-from","slug":"merging-text-transformer-models-from","title":"Merging Text Transformer Models from Different Initializations","date":"2024-03-01","arxiv_id":"2403.00986","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"1 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; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["nverma1/merging-text-transformers"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"multi-modal-heart-failure-risk-estimation","title":"Multi-modal Heart Failure Risk Estimation based on Short ECG and Sampled Long-Term HRV","date":"2024-03-01","arxiv_id":"2403.15408","n_code_links":0,"syntology":null},{"paper":"/paper/softtiger-a-clinical-foundation-model-for","slug":"softtiger-a-clinical-foundation-model-for","title":"SoftTiger: A Clinical Foundation Model for Healthcare Workflows","date":"2024-03-01","arxiv_id":"2403.00868","n_code_links":1,"syntology":null},{"paper":null,"slug":"surveying-the-dead-minds-historical","title":"Surveying the Dead Minds: Historical-Psychological Text Analysis with Contextualized Construct Representation (CCR) for Classical Chinese","date":"2024-03-01","arxiv_id":"2403.00509","n_code_links":0,"syntology":null},{"paper":"/paper/task-indicating-transformer-for-task","slug":"task-indicating-transformer-for-task","title":"Task Indicating Transformer for Task-conditional Dense Predictions","date":"2024-03-01","arxiv_id":"2403.00327","n_code_links":1,"syntology":null},{"paper":"/paper/visionllama-a-unified-llama-interface-for","slug":"visionllama-a-unified-llama-interface-for","title":"VisionLLaMA: A Unified LLaMA Backbone for Vision Tasks","date":"2024-03-01","arxiv_id":"2403.00522","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":1,"n_instrument":2,"unverified":0,"pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["meituan-automl/visionllama"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"paper":null,"slug":"a-protein-structure-prediction-approach","title":"A Protein Structure Prediction Approach Leveraging Transformer and CNN Integration","date":"2024-02-29","arxiv_id":"2402.19095","n_code_links":0,"syntology":null},{"paper":"/paper/a-simple-yet-effective-network-based-on","slug":"a-simple-yet-effective-network-based-on","title":"A Simple yet Effective Network based on Vision Transformer for Camouflaged Object and Salient Object Detection","date":"2024-02-29","arxiv_id":"2402.18922","n_code_links":1,"syntology":null},{"paper":"/paper/artist-automated-text-simplification-for-task","slug":"artist-automated-text-simplification-for-task","title":"ARTiST: Automated Text Simplification for Task Guidance in Augmented Reality","date":"2024-02-29","arxiv_id":"2402.18797","n_code_links":1,"syntology":null},{"paper":"/paper/automated-segmentation-of-lesions-and-organs","slug":"automated-segmentation-of-lesions-and-organs","title":"Automated segmentation of lesions and organs at risk on [68Ga]Ga-PSMA-11 PET/CT images using self-supervised learning with Swin UNETR","date":"2024-02-29","arxiv_id":null,"n_code_links":2,"syntology":null},{"paper":null,"slug":"crafting-knowledge-exploring-the-creative","title":"Crafting Knowledge: Exploring the Creative Mechanisms of Chat-Based Search Engines","date":"2024-02-29","arxiv_id":"2402.19421","n_code_links":0,"syntology":null},{"paper":"/paper/leveraging-pre-trained-language-models-for-3","slug":"leveraging-pre-trained-language-models-for-3","title":"Leveraging pre-trained language models for code generation","date":"2024-02-29","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/leveraging-representations-from-intermediate","slug":"leveraging-representations-from-intermediate","title":"Leveraging Representations from Intermediate Encoder-blocks for Synthetic Image Detection","date":"2024-02-29","arxiv_id":"2402.19091","n_code_links":1,"syntology":{"ran":7,"of":7,"n_ran_checked":5,"n_instrument":2,"unverified":0,"pointer_only":2,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["mever-team/rine"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"llm-ensemble-optimal-large-language-model","title":"LLM-Ensemble: Optimal Large Language Model Ensemble Method for E-commerce Product Attribute Value Extraction","date":"2024-02-29","arxiv_id":"2403.00863","n_code_links":0,"syntology":null},{"paper":null,"slug":"loss-free-machine-unlearning","title":"Loss-Free Machine Unlearning","date":"2024-02-29","arxiv_id":"2402.19308","n_code_links":0,"syntology":null},{"paper":null,"slug":"memory-augmented-generative-adversarial-1","title":"Memory-Augmented Generative Adversarial Transformers","date":"2024-02-29","arxiv_id":"2402.19218","n_code_links":0,"syntology":null},{"paper":"/paper/newsbench-systematic-evaluation-of-llms-for","slug":"newsbench-systematic-evaluation-of-llms-for","title":"NewsBench: A Systematic Evaluation Framework for Assessing Editorial Capabilities of Large Language Models in Chinese Journalism","date":"2024-02-29","arxiv_id":"2403.00862","n_code_links":1,"syntology":{"ran":0,"of":3,"n_ran_checked":0,"n_instrument":0,"unverified":3,"pointer_only":0,"phrase":"0 ran · 3 unverified","official":{"repos":["iaar-shanghai/newsbench"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":[]}}},{"paper":null,"slug":"on-the-decision-making-abilities-in-role","title":"On the Decision-Making Abilities in Role-Playing using Large Language Models","date":"2024-02-29","arxiv_id":"2402.18807","n_code_links":0,"syntology":null},{"paper":null,"slug":"paecter-patent-level-representation-learning","title":"PaECTER: Patent-level Representation Learning using Citation-informed Transformers","date":"2024-02-29","arxiv_id":"2402.19411","n_code_links":0,"syntology":null},{"paper":null,"slug":"pelle-encoder-based-language-models-for","title":"PeLLE: Encoder-based language models for Brazilian Portuguese based on open data","date":"2024-02-29","arxiv_id":"2402.19204","n_code_links":0,"syntology":null},{"paper":null,"slug":"proc2pddl-open-domain-planning","title":"PROC2PDDL: Open-Domain Planning Representations from Texts","date":"2024-02-29","arxiv_id":"2403.00092","n_code_links":0,"syntology":null},{"paper":null,"slug":"prompting-chatgpt-for-translation-a","title":"Prompting ChatGPT for Translation: A Comparative Analysis of Translation Brief and Persona Prompts","date":"2024-02-29","arxiv_id":"2403.00127","n_code_links":0,"syntology":null},{"paper":null,"slug":"query-opt-optimizing-inference-of-large","title":"Query-OPT: Optimizing Inference of Large Language Models via Multi-Query Instructions in Meeting Summarization","date":"2024-02-29","arxiv_id":"2403.00067","n_code_links":0,"syntology":null},{"paper":"/paper/retrieval-augmented-generation-for-ai","slug":"retrieval-augmented-generation-for-ai","title":"Retrieval-Augmented Generation for AI-Generated Content: A Survey","date":"2024-02-29","arxiv_id":"2402.19473","n_code_links":3,"syntology":null},{"paper":null,"slug":"rl-gpt-integrating-reinforcement-learning-and","title":"RL-GPT: Integrating Reinforcement Learning and Code-as-policy","date":"2024-02-29","arxiv_id":"2402.19299","n_code_links":0,"syntology":null},{"paper":null,"slug":"rsam-seg-a-sam-based-approach-with-prior","title":"RSAM-Seg: A SAM-based Approach with Prior Knowledge Integration for Remote Sensing Image Semantic Segmentation","date":"2024-02-29","arxiv_id":"2402.19004","n_code_links":0,"syntology":null},{"paper":"/paper/teaching-large-language-models-an-unseen","slug":"teaching-large-language-models-an-unseen","title":"Teaching Large Language Models an Unseen Language on the Fly","date":"2024-02-29","arxiv_id":"2402.19167","n_code_links":1,"syntology":{"ran":0,"of":2,"n_ran_checked":0,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"0 ran · 2 unverified","official":{"repos":["luciusssss/zhuangbench"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"paper":"/paper/timexer-empowering-transformers-for-time","slug":"timexer-empowering-transformers-for-time","title":"TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables","date":"2024-02-29","arxiv_id":"2402.19072","n_code_links":2,"syntology":{"ran":7,"of":7,"n_ran_checked":4,"n_instrument":3,"unverified":0,"pointer_only":7,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","official":{"repos":["thuml/timexer"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"vixen-visual-text-comparison-network-for","title":"VIXEN: Visual Text Comparison Network for Image Difference Captioning","date":"2024-02-29","arxiv_id":"2402.19119","n_code_links":0,"syntology":null},{"paper":null,"slug":"wisdom-of-the-silicon-crowd-llm-ensemble","title":"Wisdom of the Silicon Crowd: LLM Ensemble Prediction Capabilities Rival Human Crowd Accuracy","date":"2024-02-29","arxiv_id":"2402.19379","n_code_links":0,"syntology":null},{"paper":"/paper/x-amr-annotation-tool","slug":"x-amr-annotation-tool","title":"X-AMR Annotation Tool","date":"2024-02-29","arxiv_id":"2403.15407","n_code_links":1,"syntology":null},{"paper":null,"slug":"can-gpt-improve-the-state-of-prior","title":"Can GPT Improve the State of Prior Authorization via Guideline Based Automated Question Answering?","date":"2024-02-28","arxiv_id":"2402.18419","n_code_links":0,"syntology":null},{"paper":"/paper/clustering-and-ranking-diversity-preserved","slug":"clustering-and-ranking-diversity-preserved","title":"Clustering and Ranking: Diversity-preserved Instruction Selection through Expert-aligned Quality Estimation","date":"2024-02-28","arxiv_id":"2402.18191","n_code_links":1,"syntology":{"ran":6,"of":9,"n_ran_checked":6,"n_instrument":0,"unverified":3,"pointer_only":9,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","official":{"repos":["ironbeliever/car"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"conformer-embedding-continuous-attention-in","title":"STC-ViT: Spatio Temporal Continuous Vision Transformer for Weather Forecasting","date":"2024-02-28","arxiv_id":"2402.17966","n_code_links":0,"syntology":null},{"paper":null,"slug":"decomposed-prompting-unveiling-multilingual","title":"Decomposed Prompting: Unveiling Multilingual Linguistic Structure Knowledge in English-Centric Large Language Models","date":"2024-02-28","arxiv_id":"2402.18397","n_code_links":0,"syntology":null},{"paper":null,"slug":"ean-mapnet-efficient-vectorized-hd-map","title":"EAN-MapNet: Efficient Vectorized HD Map Construction with Anchor Neighborhoods","date":"2024-02-28","arxiv_id":"2402.18278","n_code_links":0,"syntology":null},{"paper":null,"slug":"few-shot-fairness-unveiling-llm-s-potential","title":"Few-Shot Fairness: Unveiling LLM's Potential for Fairness-Aware Classification","date":"2024-02-28","arxiv_id":"2402.18502","n_code_links":0,"syntology":null},{"paper":"/paper/fofo-a-benchmark-to-evaluate-llms-format","slug":"fofo-a-benchmark-to-evaluate-llms-format","title":"FOFO: A Benchmark to Evaluate LLMs' Format-Following Capability","date":"2024-02-28","arxiv_id":"2402.18667","n_code_links":1,"syntology":{"ran":7,"of":11,"n_ran_checked":7,"n_instrument":0,"unverified":4,"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) · 4 unverified","official":{"repos":["salesforceairesearch/fofo"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":"/paper/gradient-free-adaptive-global-pruning-for-pre","slug":"gradient-free-adaptive-global-pruning-for-pre","title":"SparseLLM: Towards Global Pruning for Pre-trained Language Models","date":"2024-02-28","arxiv_id":"2402.17946","n_code_links":2,"syntology":{"ran":3,"of":9,"n_ran_checked":2,"n_instrument":1,"unverified":6,"pointer_only":3,"phrase":"3 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 6 unverified","official":{"repos":["baithebest/adagp","baithebest/sparsellm"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":6,"ran_from_kinds":["official"]}}},{"paper":"/paper/hire-a-linguist-learning-endangered-languages","slug":"hire-a-linguist-learning-endangered-languages","title":"Hire a Linguist!: Learning Endangered Languages with In-Context Linguistic Descriptions","date":"2024-02-28","arxiv_id":"2402.18025","n_code_links":2,"syntology":{"ran":8,"of":13,"n_ran_checked":7,"n_instrument":1,"unverified":5,"pointer_only":13,"phrase":"8 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; 1 where Syntology's instrument failed) · 5 unverified","official":{"repos":["leililab/lingollm","llilab/llm4endangeredlang"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":5,"ran_from_kinds":["official"]}}},{"paper":"/paper/keeping-llms-aligned-after-fine-tuning-the","slug":"keeping-llms-aligned-after-fine-tuning-the","title":"Keeping LLMs Aligned After Fine-tuning: The Crucial Role of Prompt Templates","date":"2024-02-28","arxiv_id":"2402.18540","n_code_links":1,"syntology":{"ran":6,"of":10,"n_ran_checked":5,"n_instrument":1,"unverified":4,"pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","official":{"repos":["vfleaking/ptst"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"large-language-models-as-evolution-strategies","title":"Large Language Models As Evolution Strategies","date":"2024-02-28","arxiv_id":"2402.18381","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-associative-memories-with-gradient","title":"Learning Associative Memories with Gradient Descent","date":"2024-02-28","arxiv_id":"2402.18724","n_code_links":0,"syntology":null},{"paper":"/paper/learning-generalized-segmentation-for-foggy","slug":"learning-generalized-segmentation-for-foggy","title":"Learning Generalized Segmentation for Foggy-scenes by Bi-directional Wavelet Guidance","date":"2024-02-28","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"learning-to-deliver-a-foundation-model-for","title":"Learning to Deliver: a Foundation Model for the Montreal Capacitated Vehicle Routing Problem","date":"2024-02-28","arxiv_id":"2403.00026","n_code_links":0,"syntology":null},{"paper":null,"slug":"lemo-nade-multi-parameter-neural-architecture","title":"LeMo-NADe: Multi-Parameter Neural Architecture Discovery with LLMs","date":"2024-02-28","arxiv_id":"2402.18443","n_code_links":0,"syntology":null},{"paper":"/paper/making-them-ask-and-answer-jailbreaking-large","slug":"making-them-ask-and-answer-jailbreaking-large","title":"Making Them Ask and Answer: Jailbreaking Large Language Models in Few Queries via Disguise and Reconstruction","date":"2024-02-28","arxiv_id":"2402.18104","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"1 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; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["llm-dra/dra"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/mixer-is-more-than-just-a-model","slug":"mixer-is-more-than-just-a-model","title":"Mixer is more than just a model","date":"2024-02-28","arxiv_id":"2402.18007","n_code_links":0,"syntology":null},{"paper":"/paper/multi-objective-differentiable-neural","slug":"multi-objective-differentiable-neural","title":"Multi-objective Differentiable Neural Architecture Search","date":"2024-02-28","arxiv_id":"2402.18213","n_code_links":1,"syntology":null},{"paper":null,"slug":"objective-and-interpretable-breast-cosmesis","title":"Objective and Interpretable Breast Cosmesis Evaluation with Attention Guided Denoising Diffusion Anomaly Detection Model","date":"2024-02-28","arxiv_id":"2402.18362","n_code_links":0,"syntology":null},{"paper":null,"slug":"orchid-flexible-and-data-dependent","title":"Orchid: Flexible and Data-Dependent Convolution for Sequence Modeling","date":"2024-02-28","arxiv_id":"2402.18508","n_code_links":0,"syntology":null},{"paper":"/paper/retrieval-based-full-length-wikipedia","slug":"retrieval-based-full-length-wikipedia","title":"WIKIGENBENCH: Exploring Full-length Wikipedia Generation under Real-World Scenario","date":"2024-02-28","arxiv_id":"2402.18264","n_code_links":1,"syntology":null},{"paper":"/paper/rnns-are-not-transformers-yet-the-key","slug":"rnns-are-not-transformers-yet-the-key","title":"RNNs are not Transformers (Yet): The Key Bottleneck on In-context Retrieval","date":"2024-02-28","arxiv_id":"2402.18510","n_code_links":1,"syntology":{"ran":2,"of":3,"n_ran_checked":2,"n_instrument":0,"unverified":1,"pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["dangxingyu/rnn-icrag"],"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":null,"slug":"sftformer-a-spatial-frequency-temporal","title":"SFTformer: A Spatial-Frequency-Temporal Correlation-Decoupling Transformer for Radar Echo Extrapolation","date":"2024-02-28","arxiv_id":"2402.18044","n_code_links":0,"syntology":null},{"paper":"/paper/unsupervised-information-refinement-training","slug":"unsupervised-information-refinement-training","title":"Unsupervised Information Refinement Training of Large Language Models for Retrieval-Augmented Generation","date":"2024-02-28","arxiv_id":"2402.18150","n_code_links":1,"syntology":{"ran":7,"of":9,"n_ran_checked":7,"n_instrument":0,"unverified":2,"pointer_only":9,"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) · 2 unverified","official":{"repos":["xsc1234/info-rag"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/a-language-model-based-framework-for-new","slug":"a-language-model-based-framework-for-new","title":"A Language Model based Framework for New Concept Placement in Ontologies","date":"2024-02-27","arxiv_id":"2402.17897","n_code_links":1,"syntology":null},{"paper":null,"slug":"actrack-adding-spatio-temporal-condition-for","title":"ACTrack: Adding Spatio-Temporal Condition for Visual Object Tracking","date":"2024-02-27","arxiv_id":"2403.07914","n_code_links":0,"syntology":null},{"paper":"/paper/are-llms-capable-of-data-based-statistical","slug":"are-llms-capable-of-data-based-statistical","title":"Are LLMs Capable of Data-based Statistical and Causal Reasoning? Benchmarking Advanced Quantitative Reasoning with Data","date":"2024-02-27","arxiv_id":"2402.17644","n_code_links":1,"syntology":null},{"paper":null,"slug":"benchmarking-gpt-4-on-algorithmic-problems-a","title":"Benchmarking GPT-4 on Algorithmic Problems: A Systematic Evaluation of Prompting Strategies","date":"2024-02-27","arxiv_id":"2402.17396","n_code_links":0,"syntology":null},{"paper":null,"slug":"can-gpt-4-identify-propaganda-annotation-and","title":"Can GPT-4 Identify Propaganda? Annotation and Detection of Propaganda Spans in News Articles","date":"2024-02-27","arxiv_id":"2402.17478","n_code_links":0,"syntology":null},{"paper":"/paper/can-llm-generate-culturally-relevant","slug":"can-llm-generate-culturally-relevant","title":"Can LLM Generate Culturally Relevant Commonsense QA Data? Case Study in Indonesian and Sundanese","date":"2024-02-27","arxiv_id":"2402.17302","n_code_links":1,"syntology":{"ran":8,"of":8,"n_ran_checked":8,"n_instrument":0,"unverified":0,"pointer_only":8,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["rifkiaputri/id-csqa"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"capt-category-level-articulation-estimation","title":"CAPT: Category-level Articulation Estimation from a Single Point Cloud Using Transformer","date":"2024-02-27","arxiv_id":"2402.17360","n_code_links":0,"syntology":null},{"paper":null,"slug":"cocoa-cbt-based-conversational-counseling","title":"COCOA: CBT-based Conversational Counseling Agent using Memory Specialized in Cognitive Distortions and Dynamic Prompt","date":"2024-02-27","arxiv_id":"2402.17546","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-learning-detection-method-for-large","title":"Deep Learning Detection Method for Large Language Models-Generated Scientific Content","date":"2024-02-27","arxiv_id":"2403.00828","n_code_links":0,"syntology":null},{"paper":"/paper/ds-agent-automated-data-science-by-empowering","slug":"ds-agent-automated-data-science-by-empowering","title":"DS-Agent: Automated Data Science by Empowering Large Language Models with Case-Based Reasoning","date":"2024-02-27","arxiv_id":"2402.17453","n_code_links":1,"syntology":{"ran":9,"of":13,"n_ran_checked":9,"n_instrument":0,"unverified":4,"pointer_only":13,"phrase":"9 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; 0 where Syntology's instrument failed) · 4 unverified","official":{"repos":["guosyjlu/ds-agent"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"dual-space-optimization-improved-molecule","title":"Molecule Design by Latent Prompt Transformer","date":"2024-02-27","arxiv_id":"2402.17179","n_code_links":0,"syntology":null},{"paper":null,"slug":"emotional-voice-messages-emovome-database","title":"Emotional Voice Messages (EMOVOME) database: emotion recognition in spontaneous voice messages","date":"2024-02-27","arxiv_id":"2402.17496","n_code_links":0,"syntology":null},{"paper":"/paper/enhancing-efficiency-in-sparse-models-with","slug":"enhancing-efficiency-in-sparse-models-with","title":"XMoE: Sparse Models with Fine-grained and Adaptive Expert Selection","date":"2024-02-27","arxiv_id":"2403.18926","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["ysngki/xmoe"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/evaluating-very-long-term-conversational","slug":"evaluating-very-long-term-conversational","title":"Evaluating Very Long-Term Conversational Memory of LLM Agents","date":"2024-02-27","arxiv_id":"2402.17753","n_code_links":1,"syntology":{"ran":3,"of":7,"n_ran_checked":3,"n_instrument":0,"unverified":4,"pointer_only":7,"phrase":"3 ran (of which 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified; every one of the 3 samples that ran constructed an object rather than computing a result","official":null}},{"paper":"/paper/feature-re-embedding-towards-foundation-model","slug":"feature-re-embedding-towards-foundation-model","title":"Feature Re-Embedding: Towards Foundation Model-Level Performance in Computational Pathology","date":"2024-02-27","arxiv_id":"2402.17228","n_code_links":2,"syntology":{"ran":13,"of":24,"n_ran_checked":11,"n_instrument":2,"unverified":11,"pointer_only":24,"phrase":"13 ran (of which 6 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 2 where Syntology's instrument failed) · 11 unverified","official":{"repos":["dearcaat/rrt-mil"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":6,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/follow-my-instruction-and-spill-the-beans","slug":"follow-my-instruction-and-spill-the-beans","title":"Follow My Instruction and Spill the Beans: Scalable Data Extraction from Retrieval-Augmented Generation Systems","date":"2024-02-27","arxiv_id":"2402.17840","n_code_links":1,"syntology":{"ran":5,"of":5,"n_ran_checked":3,"n_instrument":2,"unverified":0,"pointer_only":5,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["zhentingqi/rag-privacy"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/fraud-detection-with-binding-global-and-local","slug":"fraud-detection-with-binding-global-and-local","title":"RAGFormer: Learning Semantic Attributes and Topological Structure for Fraud Detection","date":"2024-02-27","arxiv_id":"2402.17472","n_code_links":1,"syntology":null},{"paper":"/paper/handgcat-occlusion-robust-3d-hand-mesh","slug":"handgcat-occlusion-robust-3d-hand-mesh","title":"HandGCAT: Occlusion-Robust 3D Hand Mesh Reconstruction from Monocular Images","date":"2024-02-27","arxiv_id":"2403.07912","n_code_links":1,"syntology":{"ran":9,"of":9,"n_ran_checked":6,"n_instrument":3,"unverified":0,"pointer_only":9,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","official":{"repos":["heartstrive/handgcat"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"how-we-won-brats-2023-adult-glioma-challenge","title":"How we won BraTS 2023 Adult Glioma challenge? Just faking it! Enhanced Synthetic Data Augmentation and Model Ensemble for brain tumour segmentation","date":"2024-02-27","arxiv_id":"2402.17317","n_code_links":0,"syntology":null},{"paper":"/paper/intensive-care-as-one-big-sequence-modeling","slug":"intensive-care-as-one-big-sequence-modeling","title":"Intensive Care as One Big Sequence Modeling Problem","date":"2024-02-27","arxiv_id":"2402.17501","n_code_links":1,"syntology":null},{"paper":"/paper/jmlr-joint-medical-llm-and-retrieval-training","slug":"jmlr-joint-medical-llm-and-retrieval-training","title":"JMLR: Joint Medical LLM and Retrieval Training for Enhancing Reasoning and Professional Question Answering Capability","date":"2024-02-27","arxiv_id":"2402.17887","n_code_links":1,"syntology":null},{"paper":null,"slug":"latent-attention-for-linear-time-transformers","title":"Latte: Latent Attention for Linear Time Transformers","date":"2024-02-27","arxiv_id":"2402.17512","n_code_links":0,"syntology":null},{"paper":"/paper/linguistic-knowledge-can-enhance-encoder","slug":"linguistic-knowledge-can-enhance-encoder","title":"Linguistic Knowledge Can Enhance Encoder-Decoder Models (If You Let It)","date":"2024-02-27","arxiv_id":"2402.17608","n_code_links":1,"syntology":null},{"paper":"/paper/measuring-vision-language-stem-skills-of","slug":"measuring-vision-language-stem-skills-of","title":"Measuring Vision-Language STEM Skills of Neural Models","date":"2024-02-27","arxiv_id":"2402.17205","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":0,"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":["stemdataset/STEM"],"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":"model-free-deep-deterministic-policy-gradient","title":"Model Free Deep Deterministic Policy Gradient Controller for Setpoint Tracking of Non-minimum Phase Systems","date":"2024-02-27","arxiv_id":"2402.17703","n_code_links":0,"syntology":null},{"paper":"/paper/multi-task-media-bias-analysis-generalization","slug":"multi-task-media-bias-analysis-generalization","title":"MAGPIE: Multi-Task Media-Bias Analysis Generalization for Pre-Trained Identification of Expressions","date":"2024-02-27","arxiv_id":"2403.07910","n_code_links":1,"syntology":null},{"paper":null,"slug":"omniact-a-dataset-and-benchmark-for-enabling","title":"OmniACT: A Dataset and Benchmark for Enabling Multimodal Generalist Autonomous Agents for Desktop and Web","date":"2024-02-27","arxiv_id":"2402.17553","n_code_links":0,"syntology":null},{"paper":"/paper/rear-a-relevance-aware-retrieval-augmented","slug":"rear-a-relevance-aware-retrieval-augmented","title":"REAR: A Relevance-Aware Retrieval-Augmented Framework for Open-Domain Question Answering","date":"2024-02-27","arxiv_id":"2402.17497","n_code_links":1,"syntology":{"ran":5,"of":14,"n_ran_checked":5,"n_instrument":0,"unverified":9,"pointer_only":14,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 9 unverified","official":{"repos":["rucaibox/rear"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":9,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"reasoning-in-conversation-solving-subjective","title":"Reasoning in Conversation: Solving Subjective Tasks through Dialogue Simulation for Large Language Models","date":"2024-02-27","arxiv_id":"2402.17226","n_code_links":0,"syntology":null},{"paper":"/paper/researchy-questions-a-dataset-of-multi","slug":"researchy-questions-a-dataset-of-multi","title":"Researchy Questions: A Dataset of Multi-Perspective, Decompositional Questions for LLM Web Agents","date":"2024-02-27","arxiv_id":"2402.17896","n_code_links":0,"syntology":null},{"paper":null,"slug":"sdr-former-a-siamese-dual-resolution","title":"SDR-Former: A Siamese Dual-Resolution Transformer for Liver Lesion Classification Using 3D Multi-Phase Imaging","date":"2024-02-27","arxiv_id":"2402.17246","n_code_links":0,"syntology":null},{"paper":null,"slug":"skt5scisumm-a-hybrid-generative-approach-for","title":"SKT5SciSumm -- Revisiting Extractive-Generative Approach for Multi-Document Scientific Summarization","date":"2024-02-27","arxiv_id":"2402.17311","n_code_links":0,"syntology":null}],"record_sha256":"d8f04f50444581eeb55a553947b1b3c3a98f29fba776964c41166df18dbfcbfb","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}