{"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/bpe/papers/82","list_of":"/method/bpe","method":"BPE","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":82,"pages_in_order":190,"rows_per_page":100,"rows":[8101,8200],"of":18975,"counts":{"archive_papers_tagged":18975,"with_a_code_link":8675,"where_syntology_ran_a_sample":2895,"not_listed_spam_title":0,"listed":18975,"listed_where_code_ran":2895,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":2443,"every_run_a_failure_of_syntologys_instrument":452,"listed_with_a_run_with_no_instrument_failure":2443,"listed_every_run_a_failure_of_syntologys_instrument":452,"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/bpe","prev":"/method/bpe/papers/81","next":"/method/bpe/papers/83","papers":[{"paper":"/paper/scribformer-transformer-makes-cnn-work-better","slug":"scribformer-transformer-makes-cnn-work-better","title":"ScribFormer: Transformer Makes CNN Work Better for Scribble-based Medical Image Segmentation","date":"2024-02-03","arxiv_id":"2402.02029","n_code_links":1,"syntology":null},{"paper":null,"slug":"tci-former-thermal-conduction-inspired","title":"TCI-Former: Thermal Conduction-Inspired Transformer for Infrared Small Target Detection","date":"2024-02-03","arxiv_id":"2402.02046","n_code_links":0,"syntology":null},{"paper":"/paper/topology-informed-graph-transformer","slug":"topology-informed-graph-transformer","title":"Topology-Informed Graph Transformer","date":"2024-02-03","arxiv_id":"2402.02005","n_code_links":2,"syntology":{"ran":17,"of":21,"n_ran_checked":17,"n_instrument":0,"unverified":4,"pointer_only":21,"phrase":"17 ran (of which 0 constructed an object rather than computing a result; 17 with no instrument failure: 0 honoured, 2 violated, 15 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","official":{"repos":["leemingo/tigt"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/tsis-a-supplementary-algorithm-to-t-smiles","slug":"tsis-a-supplementary-algorithm-to-t-smiles","title":"Hierarchical Structure Enhances the Convergence and Generalizability of Linear Molecular Representation","date":"2024-02-03","arxiv_id":"2402.02164","n_code_links":1,"syntology":null},{"paper":"/paper/a-data-driven-analysis-of-robust-automatic","slug":"a-data-driven-analysis-of-robust-automatic","title":"A Data-Driven Analysis of Robust Automatic Piano Transcription","date":"2024-02-02","arxiv_id":"2402.01424","n_code_links":0,"syntology":null},{"paper":"/paper/alert-transformer-bridging-asynchronous-and","slug":"alert-transformer-bridging-asynchronous-and","title":"ALERT-Transformer: Bridging Asynchronous and Synchronous Machine Learning for Real-Time Event-based Spatio-Temporal Data","date":"2024-02-02","arxiv_id":"2402.01393","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-introduction-to-graphical-tensor-notation","title":"An introduction to graphical tensor notation for mechanistic interpretability","date":"2024-02-02","arxiv_id":"2402.01790","n_code_links":0,"syntology":null},{"paper":null,"slug":"bat-learning-to-reason-about-spatial-sounds","title":"BAT: Learning to Reason about Spatial Sounds with Large Language Models","date":"2024-02-02","arxiv_id":"2402.01591","n_code_links":0,"syntology":null},{"paper":null,"slug":"comet-generating-commit-messages-using-delta","title":"COMET: Generating Commit Messages using Delta Graph Context Representation","date":"2024-02-02","arxiv_id":"2402.01841","n_code_links":0,"syntology":null},{"paper":"/paper/cross-view-masked-diffusion-transformers-for","slug":"cross-view-masked-diffusion-transformers-for","title":"Cross-view Masked Diffusion Transformers for Person Image Synthesis","date":"2024-02-02","arxiv_id":"2402.01516","n_code_links":1,"syntology":{"ran":2,"of":5,"n_ran_checked":2,"n_instrument":0,"unverified":3,"pointer_only":0,"phrase":"2 ran (of which 2 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) · 3 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","official":{"repos":["trungpx/xmdpt"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":"/paper/exploring-the-limitations-of-graph-reasoning","slug":"exploring-the-limitations-of-graph-reasoning","title":"Can LLMs perform structured graph reasoning?","date":"2024-02-02","arxiv_id":"2402.01805","n_code_links":1,"syntology":null},{"paper":null,"slug":"faster-inference-of-integer-swin-transformer","title":"Faster Inference of Integer SWIN Transformer by Removing the GELU Activation","date":"2024-02-02","arxiv_id":"2402.01169","n_code_links":0,"syntology":null},{"paper":null,"slug":"how-can-generative-ai-enhance-the-well-being","title":"How Can Generative AI Enhance the Well-being of Blind?","date":"2024-02-02","arxiv_id":"2402.07919","n_code_links":0,"syntology":null},{"paper":"/paper/improving-sequential-recommendations-with","slug":"improving-sequential-recommendations-with","title":"Improving Sequential Recommendations with LLMs","date":"2024-02-02","arxiv_id":"2402.01339","n_code_links":1,"syntology":{"ran":1,"of":3,"n_ran_checked":1,"n_instrument":0,"unverified":2,"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) · 2 unverified","official":{"repos":["dh-r/llm-sequential-recommendation"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/integrating-large-language-models-in-causal","slug":"integrating-large-language-models-in-causal","title":"Integrating Large Language Models in Causal Discovery: A Statistical Causal Approach","date":"2024-02-02","arxiv_id":"2402.01454","n_code_links":2,"syntology":null},{"paper":null,"slug":"lotr-low-tensor-rank-weight-adaptation","title":"LoTR: Low Tensor Rank Weight Adaptation","date":"2024-02-02","arxiv_id":"2402.01376","n_code_links":0,"syntology":null},{"paper":null,"slug":"retrieval-augmented-end-to-end-spoken-dialog","title":"Retrieval Augmented End-to-End Spoken Dialog Models","date":"2024-02-02","arxiv_id":"2402.01828","n_code_links":0,"syntology":null},{"paper":null,"slug":"todyformer-towards-holistic-dynamic-graph","title":"Todyformer: Towards Holistic Dynamic Graph Transformers with Structure-Aware Tokenization","date":"2024-02-02","arxiv_id":"2402.05944","n_code_links":0,"syntology":null},{"paper":null,"slug":"towards-a-unified-language-model-for","title":"CorpusLM: Towards a Unified Language Model on Corpus for Knowledge-Intensive Tasks","date":"2024-02-02","arxiv_id":"2402.01176","n_code_links":0,"syntology":null},{"paper":null,"slug":"transformers-learn-nonlinear-features-in","title":"Transformers Learn Nonlinear Features In Context: Nonconvex Mean-field Dynamics on the Attention Landscape","date":"2024-02-02","arxiv_id":"2402.01258","n_code_links":0,"syntology":null},{"paper":"/paper/travelplanner-a-benchmark-for-real-world","slug":"travelplanner-a-benchmark-for-real-world","title":"TravelPlanner: A Benchmark for Real-World Planning with Language Agents","date":"2024-02-02","arxiv_id":"2402.01622","n_code_links":2,"syntology":{"ran":17,"of":17,"n_ran_checked":13,"n_instrument":4,"unverified":0,"pointer_only":4,"phrase":"17 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 1 honoured, 0 violated, 12 with no contract checked; 4 where Syntology's instrument failed) · 0 unverified","official":{"repos":["OSU-NLP-Group/TravelPlanner"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"what-will-my-model-forget-forecasting","title":"What Will My Model Forget? Forecasting Forgotten Examples in Language Model Refinement","date":"2024-02-02","arxiv_id":"2402.01865","n_code_links":0,"syntology":null},{"paper":"/paper/dendritic-learning-incorporated-vision","slug":"dendritic-learning-incorporated-vision","title":"Dendritic Learning-incorporated Vision Transformer for Image Recognition","date":"2024-02-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"fuseformer-a-transformer-for-visual-and","title":"FuseFormer: A Transformer for Visual and Thermal Image Fusion","date":"2024-02-01","arxiv_id":"2402.00971","n_code_links":0,"syntology":null},{"paper":null,"slug":"generation-distillation-and-evaluation-of","title":"Generation, Distillation and Evaluation of Motivational Interviewing-Style Reflections with a Foundational Language Model","date":"2024-02-01","arxiv_id":"2402.01051","n_code_links":0,"syntology":null},{"paper":null,"slug":"hierarchical-multi-label-classification-of-2","title":"Hierarchical Multi-Label Classification of Online Vaccine Concerns","date":"2024-02-01","arxiv_id":"2402.01783","n_code_links":0,"syntology":null},{"paper":null,"slug":"hiqa-a-hierarchical-contextual-augmentation","title":"HiQA: A Hierarchical Contextual Augmentation RAG for Multi-Documents QA","date":"2024-02-01","arxiv_id":"2402.01767","n_code_links":0,"syntology":null},{"paper":"/paper/improving-semantic-control-in-discrete-latent","slug":"improving-semantic-control-in-discrete-latent","title":"Improving Semantic Control in Discrete Latent Spaces with Transformer Quantized Variational Autoencoders","date":"2024-02-01","arxiv_id":"2402.00723","n_code_links":1,"syntology":null},{"paper":null,"slug":"learning-planning-based-reasoning-by","title":"Learning Planning-based Reasoning by Trajectories Collection and Process Reward Synthesizing","date":"2024-02-01","arxiv_id":"2402.00658","n_code_links":0,"syntology":null},{"paper":"/paper/merging-multi-task-models-via-weight","slug":"merging-multi-task-models-via-weight","title":"Merging Multi-Task Models via Weight-Ensembling Mixture of Experts","date":"2024-02-01","arxiv_id":"2402.00433","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: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["tanganke/weight-ensembling_moe"],"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":"/paper/multivariate-probabilistic-time-series","slug":"multivariate-probabilistic-time-series","title":"Multivariate Probabilistic Time Series Forecasting with Correlated Errors","date":"2024-02-01","arxiv_id":"2402.01000","n_code_links":1,"syntology":{"ran":3,"of":4,"n_ran_checked":0,"n_instrument":3,"unverified":1,"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) · 1 unverified","official":{"repos":["rottenivy/mv_pts_correlatederr"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"ocassionally-secure-a-comparative-analysis-of","title":"Ocassionally Secure: A Comparative Analysis of Code Generation Assistants","date":"2024-02-01","arxiv_id":"2402.00689","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-the-psychology-of-gpt-4-moderately-anxious","title":"On the Psychology of GPT-4: Moderately anxious, slightly masculine, honest, and humble","date":"2024-02-01","arxiv_id":"2402.01777","n_code_links":0,"syntology":null},{"paper":"/paper/recurrent-transformers-with-dynamic-halt","slug":"recurrent-transformers-with-dynamic-halt","title":"Investigating Recurrent Transformers with Dynamic Halt","date":"2024-02-01","arxiv_id":"2402.00976","n_code_links":1,"syntology":null},{"paper":null,"slug":"self-supervised-contrastive-pre-training-for-1","title":"Self-Supervised Contrastive Pre-Training for Multivariate Point Processes","date":"2024-02-01","arxiv_id":"2402.00987","n_code_links":0,"syntology":null},{"paper":"/paper/sparql-generation-with-entity-pre-trained-gpt","slug":"sparql-generation-with-entity-pre-trained-gpt","title":"SPARQL Generation with Entity Pre-trained GPT for KG Question Answering","date":"2024-02-01","arxiv_id":"2402.00969","n_code_links":1,"syntology":null},{"paper":null,"slug":"tiny-titans-can-smaller-large-language-models","title":"Tiny Titans: Can Smaller Large Language Models Punch Above Their Weight in the Real World for Meeting Summarization?","date":"2024-02-01","arxiv_id":"2402.00841","n_code_links":0,"syntology":null},{"paper":null,"slug":"towards-scalable-robotic-intervention-of","title":"Human-mediated Large Language Models for Robotic Intervention in Children with Autism Spectrum Disorders","date":"2024-02-01","arxiv_id":"2402.00260","n_code_links":0,"syntology":null},{"paper":null,"slug":"understanding-the-expressive-power-and","title":"Understanding the Expressive Power and Mechanisms of Transformer for Sequence Modeling","date":"2024-02-01","arxiv_id":"2402.00522","n_code_links":0,"syntology":null},{"paper":null,"slug":"code-aware-prompting-a-study-of-coverage","title":"Code-Aware Prompting: A study of Coverage Guided Test Generation in Regression Setting using LLM","date":"2024-01-31","arxiv_id":"2402.00097","n_code_links":0,"syntology":null},{"paper":null,"slug":"computation-and-parameter-efficient-multi","title":"Computation and Parameter Efficient Multi-Modal Fusion Transformer for Cued Speech Recognition","date":"2024-01-31","arxiv_id":"2401.17604","n_code_links":0,"syntology":null},{"paper":"/paper/consmax-hardware-friendly-alternative-softmax","slug":"consmax-hardware-friendly-alternative-softmax","title":"ConSmax: Hardware-Friendly Alternative Softmax with Learnable Parameters","date":"2024-01-31","arxiv_id":"2402.10930","n_code_links":1,"syntology":null},{"paper":null,"slug":"document-structure-in-long-document","title":"Document Structure in Long Document Transformers","date":"2024-01-31","arxiv_id":"2401.17658","n_code_links":0,"syntology":null},{"paper":"/paper/enclap-combining-neural-audio-codec-and-audio","slug":"enclap-combining-neural-audio-codec-and-audio","title":"EnCLAP: Combining Neural Audio Codec and Audio-Text Joint Embedding for Automated Audio Captioning","date":"2024-01-31","arxiv_id":"2401.17690","n_code_links":1,"syntology":null},{"paper":null,"slug":"exploring-the-limits-of-decoder-only-models","title":"Exploring the limits of decoder-only models trained on public speech recognition corpora","date":"2024-01-31","arxiv_id":"2402.00235","n_code_links":0,"syntology":null},{"paper":null,"slug":"global-liar-factuality-of-llms-over-time-and","title":"Global-Liar: Factuality of LLMs over Time and Geographic Regions","date":"2024-01-31","arxiv_id":"2401.17839","n_code_links":0,"syntology":null},{"paper":"/paper/graph-transformers-without-positional","slug":"graph-transformers-without-positional","title":"Graph Transformers without Positional Encodings","date":"2024-01-31","arxiv_id":"2401.17791","n_code_links":0,"syntology":null},{"paper":null,"slug":"head-and-neck-tumor-segmentation-from-18f-f","title":"Head and Neck Tumor Segmentation from [18F]F-FDG PET/CT Images Based on 3D Diffusion Model","date":"2024-01-31","arxiv_id":"2401.17593","n_code_links":0,"syntology":null},{"paper":"/paper/leveraging-swin-transformer-for-local-to","slug":"leveraging-swin-transformer-for-local-to","title":"Leveraging Swin Transformer for Local-to-Global Weakly Supervised Semantic Segmentation","date":"2024-01-31","arxiv_id":"2401.17828","n_code_links":1,"syntology":null},{"paper":"/paper/llm-voting-human-choices-and-ai-collective","slug":"llm-voting-human-choices-and-ai-collective","title":"LLM Voting: Human Choices and AI Collective Decision Making","date":"2024-01-31","arxiv_id":"2402.01766","n_code_links":1,"syntology":{"ran":0,"of":2,"n_ran_checked":0,"n_instrument":0,"unverified":2,"pointer_only":2,"phrase":"0 ran · 2 unverified","official":{"repos":["ethz-coss/LLM_voting"],"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/local-feature-matching-using-deep-learning-a","slug":"local-feature-matching-using-deep-learning-a","title":"Local Feature Matching Using Deep Learning: A Survey","date":"2024-01-31","arxiv_id":"2401.17592","n_code_links":1,"syntology":null},{"paper":null,"slug":"making-a-long-story-short-in-conversation","title":"Making a Long Story Short in Conversation Modeling","date":"2024-01-31","arxiv_id":"2402.00143","n_code_links":0,"syntology":null},{"paper":null,"slug":"mitigating-the-problem-of-strong-priors-in","title":"Mitigating the Influence of Distractor Tasks in LMs with Prior-Aware Decoding","date":"2024-01-31","arxiv_id":"2401.17692","n_code_links":0,"syntology":null},{"paper":null,"slug":"paramanu-a-family-of-novel-efficient-indic","title":"Paramanu: A Family of Novel Efficient Generative Foundation Language Models for Indian Languages","date":"2024-01-31","arxiv_id":"2401.18034","n_code_links":0,"syntology":null},{"paper":"/paper/positional-encoding-helps-recurrent-neural","slug":"positional-encoding-helps-recurrent-neural","title":"Positional Encoding Helps Recurrent Neural Networks Handle a Large Vocabulary","date":"2024-01-31","arxiv_id":"2402.00236","n_code_links":1,"syntology":null},{"paper":null,"slug":"rag-fusion-a-new-take-on-retrieval-augmented","title":"RAG-Fusion: a New Take on Retrieval-Augmented Generation","date":"2024-01-31","arxiv_id":"2402.03367","n_code_links":0,"syntology":null},{"paper":"/paper/raptor-recursive-abstractive-processing-for","slug":"raptor-recursive-abstractive-processing-for","title":"RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval","date":"2024-01-31","arxiv_id":"2401.18059","n_code_links":3,"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":["parthsarthi03/RAPTOR"],"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":null,"slug":"real-sparks-of-artificial-intelligence-and","title":"Real Sparks of Artificial Intelligence and the Importance of Inner Interpretability","date":"2024-01-31","arxiv_id":"2402.00901","n_code_links":0,"syntology":null},{"paper":"/paper/scape-searching-conceptual-architecture","slug":"scape-searching-conceptual-architecture","title":"SCAPE: Searching Conceptual Architecture Prompts using Evolution","date":"2024-01-31","arxiv_id":"2402.00089","n_code_links":1,"syntology":null},{"paper":null,"slug":"scavenging-hyena-distilling-transformers-into","title":"Scavenging Hyena: Distilling Transformers into Long Convolution Models","date":"2024-01-31","arxiv_id":"2401.17574","n_code_links":0,"syntology":null},{"paper":null,"slug":"supporting-anticipatory-governance-using-llms","title":"Evaluating the Capabilities of LLMs for Supporting Anticipatory Impact Assessment","date":"2024-01-31","arxiv_id":"2401.18028","n_code_links":0,"syntology":null},{"paper":"/paper/swea-changing-factual-knowledge-in-large","slug":"swea-changing-factual-knowledge-in-large","title":"SWEA: Updating Factual Knowledge in Large Language Models via Subject Word Embedding Altering","date":"2024-01-31","arxiv_id":"2401.17809","n_code_links":1,"syntology":{"ran":3,"of":6,"n_ran_checked":2,"n_instrument":1,"unverified":3,"pointer_only":6,"phrase":"3 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; 1 where Syntology's instrument failed) · 3 unverified","official":{"repos":["xpq-tech/swea"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"uncertainty-aware-explainable-recommendation","title":"Uncertainty-Aware Explainable Recommendation with Large Language Models","date":"2024-01-31","arxiv_id":"2402.03366","n_code_links":0,"syntology":null},{"paper":"/paper/wsc-enhancing-the-winograd-schema-challenge","slug":"wsc-enhancing-the-winograd-schema-challenge","title":"WSC+: Enhancing The Winograd Schema Challenge Using Tree-of-Experts","date":"2024-01-31","arxiv_id":"2401.17703","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-cross-language-investigation-into-jailbreak","title":"A Cross-Language Investigation into Jailbreak Attacks in Large Language Models","date":"2024-01-30","arxiv_id":"2401.16765","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-preliminary-study-on-using-large-language","title":"A Preliminary Study on Using Large Language Models in Software Pentesting","date":"2024-01-30","arxiv_id":"2401.17459","n_code_links":0,"syntology":null},{"paper":"/paper/breaking-free-transformer-models-task","slug":"breaking-free-transformer-models-task","title":"Breaking Free Transformer Models: Task-specific Context Attribution Promises Improved Generalizability Without Fine-tuning Pre-trained LLMs","date":"2024-01-30","arxiv_id":"2401.16638","n_code_links":1,"syntology":null},{"paper":null,"slug":"cafct-contextual-and-attentional-feature","title":"CAFCT-Net: A CNN-Transformer Hybrid Network with Contextual and Attentional Feature Fusion for Liver Tumor Segmentation","date":"2024-01-30","arxiv_id":"2401.16886","n_code_links":0,"syntology":null},{"paper":"/paper/conditional-and-modal-reasoning-in-large","slug":"conditional-and-modal-reasoning-in-large","title":"Conditional and Modal Reasoning in Large Language Models","date":"2024-01-30","arxiv_id":"2401.17169","n_code_links":1,"syntology":null},{"paper":"/paper/crud-rag-a-comprehensive-chinese-benchmark","slug":"crud-rag-a-comprehensive-chinese-benchmark","title":"CRUD-RAG: A Comprehensive Chinese Benchmark for Retrieval-Augmented Generation of Large Language Models","date":"2024-01-30","arxiv_id":"2401.17043","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":["iaar-shanghai/crud_rag"],"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":null,"slug":"large-multi-modal-models-lmms-as-universal","title":"Large Multi-Modal Models (LMMs) as Universal Foundation Models for AI-Native Wireless Systems","date":"2024-01-30","arxiv_id":"2402.01748","n_code_links":0,"syntology":null},{"paper":"/paper/llamp-large-language-model-made-powerful-for","slug":"llamp-large-language-model-made-powerful-for","title":"LLaMP: Large Language Model Made Powerful for High-fidelity Materials Knowledge Retrieval and Distillation","date":"2024-01-30","arxiv_id":"2401.17244","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":["chiang-yuan/llamp"],"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":"/paper/mt-eval-a-multi-turn-capabilities-evaluation","slug":"mt-eval-a-multi-turn-capabilities-evaluation","title":"MT-Eval: A Multi-Turn Capabilities Evaluation Benchmark for Large Language Models","date":"2024-01-30","arxiv_id":"2401.16745","n_code_links":1,"syntology":{"ran":9,"of":11,"n_ran_checked":9,"n_instrument":0,"unverified":2,"pointer_only":2,"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) · 2 unverified","official":{"repos":["kwanwaichung/mt-eval"],"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/optistate-state-estimation-of-legged-robots","slug":"optistate-state-estimation-of-legged-robots","title":"OptiState: State Estimation of Legged Robots using Gated Networks with Transformer-based Vision and Kalman Filtering","date":"2024-01-30","arxiv_id":"2401.16719","n_code_links":1,"syntology":null},{"paper":"/paper/owsm-v3-1-better-and-faster-open-whisper","slug":"owsm-v3-1-better-and-faster-open-whisper","title":"OWSM v3.1: Better and Faster Open Whisper-Style Speech Models based on E-Branchformer","date":"2024-01-30","arxiv_id":"2401.16658","n_code_links":1,"syntology":null},{"paper":null,"slug":"performance-assessment-of-chatgpt-vs-bard-in","title":"Performance Assessment of ChatGPT vs Bard in Detecting Alzheimer's Dementia","date":"2024-01-30","arxiv_id":"2402.01751","n_code_links":0,"syntology":null},{"paper":"/paper/robust-prompt-optimization-for-defending","slug":"robust-prompt-optimization-for-defending","title":"Robust Prompt Optimization for Defending Language Models Against Jailbreaking Attacks","date":"2024-01-30","arxiv_id":"2401.17263","n_code_links":1,"syntology":{"ran":3,"of":4,"n_ran_checked":0,"n_instrument":3,"unverified":1,"pointer_only":4,"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) · 1 unverified","official":{"repos":["lapisrocks/rpo"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/synthetic-dialogue-dataset-generation-using","slug":"synthetic-dialogue-dataset-generation-using","title":"Synthetic Dialogue Dataset Generation using LLM Agents","date":"2024-01-30","arxiv_id":"2401.17461","n_code_links":1,"syntology":null},{"paper":null,"slug":"t3-transparent-tracking-triggering-for-fine","title":"T3: Transparent Tracking & Triggering for Fine-grained Overlap of Compute & Collectives","date":"2024-01-30","arxiv_id":"2401.16677","n_code_links":0,"syntology":null},{"paper":null,"slug":"weaver-foundation-models-for-creative-writing","title":"Weaver: Foundation Models for Creative Writing","date":"2024-01-30","arxiv_id":"2401.17268","n_code_links":0,"syntology":null},{"paper":"/paper/3dg-a-framework-for-using-generative-ai-for","slug":"3dg-a-framework-for-using-generative-ai-for","title":"3DG: A Framework for Using Generative AI for Handling Sparse Learner Performance Data From Intelligent Tutoring Systems","date":"2024-01-29","arxiv_id":"2402.01746","n_code_links":1,"syntology":null},{"paper":"/paper/a-gated-mlp-architecture-for-learning","slug":"a-gated-mlp-architecture-for-learning","title":"Enhancing Topological Dependencies in Spatio-Temporal Graphs with Cycle Message Passing Blocks","date":"2024-01-29","arxiv_id":"2401.15894","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-survey-on-structure-preserving-graph","title":"A Survey on Structure-Preserving Graph Transformers","date":"2024-01-29","arxiv_id":"2401.16176","n_code_links":0,"syntology":null},{"paper":null,"slug":"context-former-stitching-via-latent","title":"Context-Former: Stitching via Latent Conditioned Sequence Modeling","date":"2024-01-29","arxiv_id":"2401.16452","n_code_links":0,"syntology":null},{"paper":null,"slug":"development-and-testing-of-a-novel-large","title":"Development and Testing of a Novel Large Language Model-Based Clinical Decision Support Systems for Medication Safety in 12 Clinical Specialties","date":"2024-01-29","arxiv_id":"2402.01741","n_code_links":0,"syntology":null},{"paper":null,"slug":"development-and-testing-of-retrieval","title":"Development and Testing of Retrieval Augmented Generation in Large Language Models -- A Case Study Report","date":"2024-01-29","arxiv_id":"2402.01733","n_code_links":0,"syntology":null},{"paper":null,"slug":"diverse-but-divisive-llms-can-exaggerate","title":"Diverse, but Divisive: LLMs Can Exaggerate Gender Differences in Opinion Related to Harms of Misinformation","date":"2024-01-29","arxiv_id":"2401.16558","n_code_links":0,"syntology":null},{"paper":"/paper/e-eval-a-comprehensive-chinese-k-12-education","slug":"e-eval-a-comprehensive-chinese-k-12-education","title":"E-EVAL: A Comprehensive Chinese K-12 Education Evaluation Benchmark for Large Language Models","date":"2024-01-29","arxiv_id":"2401.15927","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":1,"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":["ai-edu-lab/e-eval"],"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":"hybrid-transformer-and-spatial-temporal-self","title":"Hybrid Transformer and Spatial-Temporal Self-Supervised Learning for Long-term Traffic Prediction","date":"2024-01-29","arxiv_id":"2401.16453","n_code_links":0,"syntology":null},{"paper":null,"slug":"leveraging-professional-radiologists","title":"Leveraging Professional Radiologists' Expertise to Enhance LLMs' Evaluation for Radiology Reports","date":"2024-01-29","arxiv_id":"2401.16578","n_code_links":0,"syntology":null},{"paper":null,"slug":"llava-mole-sparse-mixture-of-lora-experts-for","title":"LLaVA-MoLE: Sparse Mixture of LoRA Experts for Mitigating Data Conflicts in Instruction Finetuning MLLMs","date":"2024-01-29","arxiv_id":"2401.16160","n_code_links":0,"syntology":null},{"paper":null,"slug":"llm4vuln-a-unified-evaluation-framework-for","title":"LLM4Vuln: A Unified Evaluation Framework for Decoupling and Enhancing LLMs' Vulnerability Reasoning","date":"2024-01-29","arxiv_id":"2401.16185","n_code_links":0,"syntology":null},{"paper":null,"slug":"prompt4vis-prompting-large-language-models","title":"Prompt4Vis: Prompting Large Language Models with Example Mining and Schema Filtering for Tabular Data Visualization","date":"2024-01-29","arxiv_id":"2402.07909","n_code_links":0,"syntology":null},{"paper":"/paper/regal-refactoring-programs-to-discover","slug":"regal-refactoring-programs-to-discover","title":"ReGAL: Refactoring Programs to Discover Generalizable Abstractions","date":"2024-01-29","arxiv_id":"2401.16467","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"pointer_only":2,"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":["esteng/regal_program_learning"],"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":null,"slug":"response-generation-for-cognitive-behavioral","title":"Response Generation for Cognitive Behavioral Therapy with Large Language Models: Comparative Study with Socratic Questioning","date":"2024-01-29","arxiv_id":"2401.15966","n_code_links":0,"syntology":null},{"paper":null,"slug":"security-code-review-by-llms-a-deep-dive-into","title":"An Insight into Security Code Review with LLMs: Capabilities, Obstacles, and Influential Factors","date":"2024-01-29","arxiv_id":"2401.16310","n_code_links":0,"syntology":null},{"paper":"/paper/shvit-single-head-vision-transformer-with","slug":"shvit-single-head-vision-transformer-with","title":"SHViT: Single-Head Vision Transformer with Memory Efficient Macro Design","date":"2024-01-29","arxiv_id":"2401.16456","n_code_links":1,"syntology":{"ran":7,"of":8,"n_ran_checked":7,"n_instrument":0,"unverified":1,"pointer_only":8,"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":["ysj9909/SHViT"],"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":"/paper/stolen-subwords-importance-of-vocabularies","slug":"stolen-subwords-importance-of-vocabularies","title":"Stolen Subwords: Importance of Vocabularies for Machine Translation Model Stealing","date":"2024-01-29","arxiv_id":"2401.16055","n_code_links":1,"syntology":null},{"paper":"/paper/topro-token-level-prompt-decomposition-for","slug":"topro-token-level-prompt-decomposition-for","title":"ToPro: Token-Level Prompt Decomposition for Cross-Lingual Sequence Labeling Tasks","date":"2024-01-29","arxiv_id":"2401.16589","n_code_links":1,"syntology":null},{"paper":null,"slug":"trackgpt-a-generative-pre-trained-transformer","title":"TrackGPT -- A generative pre-trained transformer for cross-domain entity trajectory forecasting","date":"2024-01-29","arxiv_id":"2402.00066","n_code_links":0,"syntology":null}],"record_sha256":"e40981773c9597250aa87cb4f8b3208c5eae86c4b45035d25d4b86b3393ea30e","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}