{"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/transformer/papers/65","list_of":"/method/transformer","method":"Transformer","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":65,"pages_in_order":140,"rows_per_page":100,"rows":[6401,6500],"of":13999,"counts":{"archive_papers_tagged":13999,"with_a_code_link":6572,"where_syntology_ran_a_sample":2248,"not_listed_spam_title":0,"listed":13999,"listed_where_code_ran":2248,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1919,"every_run_a_failure_of_syntologys_instrument":329,"listed_with_a_run_with_no_instrument_failure":1919,"listed_every_run_a_failure_of_syntologys_instrument":329,"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/transformer","prev":"/method/transformer/papers/64","next":"/method/transformer/papers/66","papers":[{"paper":null,"slug":"llamas-know-what-gpts-don-t-show-surrogate","title":"Llamas Know What GPTs Don't Show: Surrogate Models for Confidence Estimation","date":"2023-11-15","arxiv_id":"2311.08877","n_code_links":0,"syntology":null},{"paper":"/paper/mela-multilingual-evaluation-of-linguistic","slug":"mela-multilingual-evaluation-of-linguistic","title":"MELA: Multilingual Evaluation of Linguistic Acceptability","date":"2023-11-15","arxiv_id":"2311.09033","n_code_links":1,"syntology":null},{"paper":"/paper/progressive-feedback-enhanced-transformer-for","slug":"progressive-feedback-enhanced-transformer-for","title":"Progressive Feedback-Enhanced Transformer for Image Forgery Localization","date":"2023-11-15","arxiv_id":"2311.08910","n_code_links":1,"syntology":null},{"paper":"/paper/safer-instruct-aligning-language-models-with","slug":"safer-instruct-aligning-language-models-with","title":"Safer-Instruct: Aligning Language Models with Automated Preference Data","date":"2023-11-15","arxiv_id":"2311.08685","n_code_links":1,"syntology":null},{"paper":null,"slug":"sparsespikformer-a-co-design-framework-for","title":"SparseSpikformer: A Co-Design Framework for Token and Weight Pruning in Spiking Transformer","date":"2023-11-15","arxiv_id":"2311.08806","n_code_links":0,"syntology":null},{"paper":"/paper/token-prediction-as-implicit-classification","slug":"token-prediction-as-implicit-classification","title":"Token Prediction as Implicit Classification to Identify LLM-Generated Text","date":"2023-11-15","arxiv_id":"2311.08723","n_code_links":1,"syntology":{"ran":3,"of":6,"n_ran_checked":3,"n_instrument":0,"unverified":3,"pointer_only":0,"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) · 3 unverified","official":{"repos":["markchenyutian/t5-sentinel-public"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":"/paper/tooltalk-evaluating-tool-usage-in-a","slug":"tooltalk-evaluating-tool-usage-in-a","title":"ToolTalk: Evaluating Tool-Usage in a Conversational Setting","date":"2023-11-15","arxiv_id":"2311.10775","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":null}},{"paper":null,"slug":"x-eval-generalizable-multi-aspect-text","title":"X-Eval: Generalizable Multi-aspect Text Evaluation via Augmented Instruction Tuning with Auxiliary Evaluation Aspects","date":"2023-11-15","arxiv_id":"2311.08788","n_code_links":0,"syntology":null},{"paper":"/paper/a-wolf-in-sheep-s-clothing-generalized-nested","slug":"a-wolf-in-sheep-s-clothing-generalized-nested","title":"A Wolf in Sheep's Clothing: Generalized Nested Jailbreak Prompts can Fool Large Language Models Easily","date":"2023-11-14","arxiv_id":"2311.08268","n_code_links":1,"syntology":{"ran":8,"of":9,"n_ran_checked":7,"n_instrument":1,"unverified":1,"pointer_only":3,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 1 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["NJUNLP/ReNeLLM"],"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":"/paper/automated-title-and-abstract-screening-for","slug":"automated-title-and-abstract-screening-for","title":"Automated title and abstract screening for scoping reviews using the GPT-4 Large Language Model","date":"2023-11-14","arxiv_id":"2311.07918","n_code_links":1,"syntology":null},{"paper":null,"slug":"comparing-humans-gpt-4-and-gpt-4v-on","title":"Comparing Humans, GPT-4, and GPT-4V On Abstraction and Reasoning Tasks","date":"2023-11-14","arxiv_id":"2311.09247","n_code_links":0,"syntology":null},{"paper":"/paper/contrastive-learning-for-multi-object","slug":"contrastive-learning-for-multi-object","title":"Contrastive Learning for Multi-Object Tracking with Transformers","date":"2023-11-14","arxiv_id":"2311.08043","n_code_links":1,"syntology":null},{"paper":null,"slug":"dual-channel-prototype-network-for-few-shot","title":"Dual-channel Prototype Network for few-shot Classification of Pathological Images","date":"2023-11-14","arxiv_id":"2311.07871","n_code_links":0,"syntology":null},{"paper":null,"slug":"evaluating-llms-on-document-based-qa-exact","title":"Evaluating LLMs on Document-Based QA: Exact Answer Selection and Numerical Extraction using Cogtale dataset","date":"2023-11-14","arxiv_id":"2311.07878","n_code_links":0,"syntology":null},{"paper":"/paper/exploring-semi-supervised-hierarchical","slug":"exploring-semi-supervised-hierarchical","title":"Exploring Semi-supervised Hierarchical Stacked Encoder for Legal Judgement Prediction","date":"2023-11-14","arxiv_id":"2311.08103","n_code_links":1,"syntology":null},{"paper":"/paper/gmtr-graph-matching-transformers","slug":"gmtr-graph-matching-transformers","title":"GMTR: Graph Matching Transformers","date":"2023-11-14","arxiv_id":"2311.08141","n_code_links":1,"syntology":null},{"paper":null,"slug":"how-good-are-large-language-models-on-african","title":"How good are Large Language Models on African Languages?","date":"2023-11-14","arxiv_id":"2311.07978","n_code_links":0,"syntology":null},{"paper":"/paper/magic-benchmarking-large-language-model","slug":"magic-benchmarking-large-language-model","title":"MAgIC: Investigation of Large Language Model Powered Multi-Agent in Cognition, Adaptability, Rationality and Collaboration","date":"2023-11-14","arxiv_id":"2311.08562","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"pointer_only":0,"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":["cathyxl/magic"],"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":"rotation-agnostic-image-representation","title":"Rotation-Agnostic Image Representation Learning for Digital Pathology","date":"2023-11-14","arxiv_id":"2311.08359","n_code_links":0,"syntology":null},{"paper":"/paper/secure-transformer-inference","slug":"secure-transformer-inference","title":"Secure Transformer Inference Protocol","date":"2023-11-14","arxiv_id":"2312.00025","n_code_links":1,"syntology":{"ran":4,"of":4,"n_ran_checked":1,"n_instrument":3,"unverified":0,"pointer_only":1,"phrase":"4 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; 3 where Syntology's instrument failed) · 0 unverified","official":{"repos":["yuanmu97/secure-transformer-inference"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"paper":null,"slug":"simplesafetytests-a-test-suite-for","title":"SimpleSafetyTests: a Test Suite for Identifying Critical Safety Risks in Large Language Models","date":"2023-11-14","arxiv_id":"2311.08370","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-benchmark-to-understand-the-role-of","title":"A Benchmark to Understand the Role of Knowledge Graphs on Large Language Model's Accuracy for Question Answering on Enterprise SQL Databases","date":"2023-11-13","arxiv_id":"2311.07509","n_code_links":0,"syntology":null},{"paper":"/paper/assessing-logical-puzzle-solving-in-large","slug":"assessing-logical-puzzle-solving-in-large","title":"Assessing Logical Puzzle Solving in Large Language Models: Insights from a Minesweeper Case Study","date":"2023-11-13","arxiv_id":"2311.07387","n_code_links":1,"syntology":{"ran":6,"of":8,"n_ran_checked":6,"n_instrument":0,"unverified":2,"pointer_only":0,"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) · 2 unverified","official":{"repos":["yinghao-li/minesweeper-for-llm"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"cross-axis-transformer-with-2d-rotary","title":"Cross-Axis Transformer with 3D Rotary Positional Embeddings","date":"2023-11-13","arxiv_id":"2311.07184","n_code_links":0,"syntology":null},{"paper":"/paper/fovea-transformer-efficient-long-context","slug":"fovea-transformer-efficient-long-context","title":"Fovea Transformer: Efficient Long-Context Modeling with Structured Fine-to-Coarse Attention","date":"2023-11-13","arxiv_id":"2311.07102","n_code_links":1,"syntology":null},{"paper":null,"slug":"language-grounded-qformer-for-efficient","title":"Semantically Grounded QFormer for Efficient Vision Language Understanding","date":"2023-11-13","arxiv_id":"2311.07449","n_code_links":0,"syntology":null},{"paper":null,"slug":"lm-polygraph-uncertainty-estimation-for","title":"LM-Polygraph: Uncertainty Estimation for Language Models","date":"2023-11-13","arxiv_id":"2311.07383","n_code_links":0,"syntology":null},{"paper":null,"slug":"megaverse-benchmarking-large-language-models","title":"MEGAVERSE: Benchmarking Large Language Models Across Languages, Modalities, Models and Tasks","date":"2023-11-13","arxiv_id":"2311.07463","n_code_links":0,"syntology":null},{"paper":null,"slug":"speech-based-slot-filling-using-large","title":"Speech-based Slot Filling using Large Language Models","date":"2023-11-13","arxiv_id":"2311.07418","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-impact-of-large-language-models-on","title":"The Impact of Large Language Models on Scientific Discovery: a Preliminary Study using GPT-4","date":"2023-11-13","arxiv_id":"2311.07361","n_code_links":0,"syntology":null},{"paper":"/paper/veritymath-advancing-mathematical-reasoning","slug":"veritymath-advancing-mathematical-reasoning","title":"VerityMath: Advancing Mathematical Reasoning by Self-Verification Through Unit Consistency","date":"2023-11-13","arxiv_id":"2311.07172","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":["vernontoh/veritymath"],"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":"controllable-topic-focused-abstractive","title":"Controllable Topic-Focused Abstractive Summarization","date":"2023-11-12","arxiv_id":"2311.06724","n_code_links":0,"syntology":null},{"paper":null,"slug":"detecting-and-correcting-hate-speech-in","title":"Detecting and Correcting Hate Speech in Multimodal Memes with Large Visual Language Model","date":"2023-11-12","arxiv_id":"2311.06737","n_code_links":0,"syntology":null},{"paper":null,"slug":"evaluation-of-gpt-4-for-chest-x-ray","title":"Evaluation of GPT-4 for chest X-ray impression generation: A reader study on performance and perception","date":"2023-11-12","arxiv_id":"2311.06815","n_code_links":0,"syntology":null},{"paper":"/paper/flames-benchmarking-value-alignment-of","slug":"flames-benchmarking-value-alignment-of","title":"Flames: Benchmarking Value Alignment of LLMs in Chinese","date":"2023-11-12","arxiv_id":"2311.06899","n_code_links":1,"syntology":null},{"paper":null,"slug":"large-language-models-understanding-of-math","title":"Large Language Models' Understanding of Math: Source Criticism and Extrapolation","date":"2023-11-12","arxiv_id":"2311.07618","n_code_links":0,"syntology":null},{"paper":null,"slug":"tsvit-a-time-series-vision-transformer-for","title":"TSViT: A Time Series Vision Transformer for Fault Diagnosis","date":"2023-11-12","arxiv_id":"2311.06916","n_code_links":0,"syntology":null},{"paper":null,"slug":"two-stream-scene-understanding-on-graph","title":"Two Stream Scene Understanding on Graph Embedding","date":"2023-11-12","arxiv_id":"2311.06746","n_code_links":0,"syntology":null},{"paper":"/paper/adversarial-fine-tuning-using-generated","slug":"adversarial-fine-tuning-using-generated","title":"Adversarial Fine-tuning using Generated Respiratory Sound to Address Class Imbalance","date":"2023-11-11","arxiv_id":"2311.06480","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 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) · 0 unverified; every one of the 3 samples that ran constructed an object rather than computing a result","official":{"repos":["kaen2891/adversarial_fine-tuning_using_generated_respiratory_sound"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/cvthead-one-shot-controllable-head-avatar","slug":"cvthead-one-shot-controllable-head-avatar","title":"CVTHead: One-shot Controllable Head Avatar with Vertex-feature Transformer","date":"2023-11-11","arxiv_id":"2311.06443","n_code_links":1,"syntology":{"ran":5,"of":6,"n_ran_checked":2,"n_instrument":3,"unverified":1,"pointer_only":6,"phrase":"5 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; 3 where Syntology's instrument failed) · 1 unverified","official":{"repos":["howiema/cvthead"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"intentional-biases-in-llm-responses","title":"Intentional Biases in LLM Responses","date":"2023-11-11","arxiv_id":"2311.07611","n_code_links":0,"syntology":null},{"paper":null,"slug":"sparse-attention-based-neural-networks-for","title":"Sparse Attention-Based Neural Networks for Code Classification","date":"2023-11-11","arxiv_id":"2311.06575","n_code_links":0,"syntology":null},{"paper":"/paper/automatic-report-generation-for","slug":"automatic-report-generation-for","title":"Automatic Report Generation for Histopathology images using pre-trained Vision Transformers","date":"2023-11-10","arxiv_id":"2311.06176","n_code_links":1,"syntology":null},{"paper":"/paper/data-contamination-quiz-a-tool-to-detect-and","slug":"data-contamination-quiz-a-tool-to-detect-and","title":"Data Contamination Quiz: A Tool to Detect and Estimate Contamination in Large Language Models","date":"2023-11-10","arxiv_id":"2311.06233","n_code_links":2,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"pointer_only":0,"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":["shahriargolchin/dcq"],"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/dual-input-stream-transformer-for-eye","slug":"dual-input-stream-transformer-for-eye","title":"Dual input stream transformer for vertical drift correction in eye-tracking reading data","date":"2023-11-10","arxiv_id":"2311.06095","n_code_links":1,"syntology":null},{"paper":null,"slug":"enhancing-rock-image-segmentation-in-digital","title":"Enhancing Rock Image Segmentation in Digital Rock Physics: A Fusion of Generative AI and State-of-the-Art Neural Networks","date":"2023-11-10","arxiv_id":"2311.06079","n_code_links":0,"syntology":null},{"paper":null,"slug":"hiformer-heterogeneous-feature-interactions","title":"Hiformer: Heterogeneous Feature Interactions Learning with Transformers for Recommender Systems","date":"2023-11-10","arxiv_id":"2311.05884","n_code_links":0,"syntology":null},{"paper":null,"slug":"how-to-bridge-the-gap-between-modalities-a","title":"How to Bridge the Gap between Modalities: Survey on Multimodal Large Language Model","date":"2023-11-10","arxiv_id":"2311.07594","n_code_links":0,"syntology":null},{"paper":null,"slug":"language-models-can-be-logical-solvers","title":"Language Models can be Logical Solvers","date":"2023-11-10","arxiv_id":"2311.06158","n_code_links":0,"syntology":null},{"paper":null,"slug":"making-llms-worth-every-penny-resource","title":"Making LLMs Worth Every Penny: Resource-Limited Text Classification in Banking","date":"2023-11-10","arxiv_id":"2311.06102","n_code_links":0,"syntology":null},{"paper":"/paper/transformcode-a-contrastive-learning","slug":"transformcode-a-contrastive-learning","title":"TransformCode: A Contrastive Learning Framework for Code Embedding via Subtree Transformation","date":"2023-11-10","arxiv_id":"2311.08157","n_code_links":1,"syntology":null},{"paper":"/paper/accuracy-of-a-vision-language-model-on","slug":"accuracy-of-a-vision-language-model-on","title":"Multimodal Foundation Models Exploit Text to Make Medical Image Predictions","date":"2023-11-09","arxiv_id":"2311.05591","n_code_links":2,"syntology":null},{"paper":null,"slug":"brainnetdiff-generative-ai-empowers-brain","title":"BrainNetDiff: Generative AI Empowers Brain Network Generation via Multimodal Diffusion Model","date":"2023-11-09","arxiv_id":"2311.05199","n_code_links":0,"syntology":null},{"paper":"/paper/conic10k-a-challenging-math-problem","slug":"conic10k-a-challenging-math-problem","title":"Conic10K: A Challenging Math Problem Understanding and Reasoning Dataset","date":"2023-11-09","arxiv_id":"2311.05113","n_code_links":1,"syntology":null},{"paper":null,"slug":"do-personality-tests-generalize-to-large","title":"Challenging the Validity of Personality Tests for Large Language Models","date":"2023-11-09","arxiv_id":"2311.05297","n_code_links":0,"syntology":null},{"paper":null,"slug":"dynamic-association-learning-of-self","title":"Dynamic Association Learning of Self-Attention and Convolution in Image Restoration","date":"2023-11-09","arxiv_id":"2311.05147","n_code_links":0,"syntology":null},{"paper":null,"slug":"intelligent-cervical-spine-fracture-detection","title":"Intelligent Cervical Spine Fracture Detection Using Deep Learning Methods","date":"2023-11-09","arxiv_id":"2311.05708","n_code_links":0,"syntology":null},{"paper":null,"slug":"protein-ligand-binding-representation","title":"Protein-ligand binding representation learning from fine-grained interactions","date":"2023-11-09","arxiv_id":"2311.16160","n_code_links":0,"syntology":null},{"paper":"/paper/technical-report-large-language-models-can","slug":"technical-report-large-language-models-can","title":"Large Language Models can Strategically Deceive their Users when Put Under Pressure","date":"2023-11-09","arxiv_id":"2311.07590","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":["apolloresearch/insider-trading"],"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/vision-encoder-decoder-models-for-ai-coaching","slug":"vision-encoder-decoder-models-for-ai-coaching","title":"Vision Encoder-Decoder Models for AI Coaching","date":"2023-11-09","arxiv_id":"2311.16161","n_code_links":2,"syntology":null},{"paper":"/paper/beyond-size-how-gradients-shape-pruning","slug":"beyond-size-how-gradients-shape-pruning","title":"Beyond Size: How Gradients Shape Pruning Decisions in Large Language Models","date":"2023-11-08","arxiv_id":"2311.04902","n_code_links":2,"syntology":{"ran":3,"of":7,"n_ran_checked":2,"n_instrument":1,"unverified":4,"pointer_only":1,"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) · 4 unverified","official":{"repos":["rocktimjyotidas/gblm-pruner","vila-lab/gblm-pruner"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":"/paper/data-factors-for-better-compositional","slug":"data-factors-for-better-compositional","title":"Data Factors for Better Compositional Generalization","date":"2023-11-08","arxiv_id":"2311.04420","n_code_links":1,"syntology":{"ran":8,"of":8,"n_ran_checked":8,"n_instrument":0,"unverified":0,"pointer_only":0,"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":["owenzx/data4comp"],"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":"/paper/euclidean-projective-conformal-choosing-a","slug":"euclidean-projective-conformal-choosing-a","title":"Euclidean, Projective, Conformal: Choosing a Geometric Algebra for Equivariant Transformers","date":"2023-11-08","arxiv_id":"2311.04744","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":null}},{"paper":"/paper/fibrovit-vision-transformer-based-framework","slug":"fibrovit-vision-transformer-based-framework","title":"FibroVit—Vision transformer-based framework for detection and classification of pulmonary fibrosis from chest CT images","date":"2023-11-08","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"hybrid-focal-and-full-range-attention-based","title":"Hybrid Focal and Full-Range Attention Based Graph Transformers","date":"2023-11-08","arxiv_id":"2311.04653","n_code_links":0,"syntology":null},{"paper":"/paper/loss-masking-is-not-needed-in-decoder-only","slug":"loss-masking-is-not-needed-in-decoder-only","title":"Loss Masking Is Not Needed in Decoder-only Transformer for Discrete-token-based ASR","date":"2023-11-08","arxiv_id":"2311.04534","n_code_links":1,"syntology":null},{"paper":"/paper/rethinking-benchmark-and-contamination-for","slug":"rethinking-benchmark-and-contamination-for","title":"Rethinking Benchmark and Contamination for Language Models with Rephrased Samples","date":"2023-11-08","arxiv_id":"2311.04850","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":0,"n_instrument":3,"unverified":0,"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) · 0 unverified","official":{"repos":["lm-sys/llm-decontaminator"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/ss-mae-spatial-spectral-masked-auto-encoder","slug":"ss-mae-spatial-spectral-masked-auto-encoder","title":"SS-MAE: Spatial-Spectral Masked Auto-Encoder for Multi-Source Remote Sensing Image Classification","date":"2023-11-08","arxiv_id":"2311.04442","n_code_links":1,"syntology":{"ran":9,"of":15,"n_ran_checked":7,"n_instrument":2,"unverified":6,"pointer_only":15,"phrase":"9 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; 2 where Syntology's instrument failed) · 6 unverified","official":{"repos":["summitgao/ss-mae"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":6,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"towards-few-annotation-learning-in-computer","title":"Towards Few-Annotation Learning in Computer Vision: Application to Image Classification and Object Detection tasks","date":"2023-11-08","arxiv_id":"2311.04888","n_code_links":0,"syntology":null},{"paper":null,"slug":"vital-sign-forecasting-for-sepsis-patients-in","title":"Vital Sign Forecasting for Sepsis Patients in ICUs","date":"2023-11-08","arxiv_id":"2311.04770","n_code_links":0,"syntology":null},{"paper":"/paper/a-simple-interpretable-transformer-for-fine","slug":"a-simple-interpretable-transformer-for-fine","title":"A Simple Interpretable Transformer for Fine-Grained Image Classification and Analysis","date":"2023-11-07","arxiv_id":"2311.04157","n_code_links":1,"syntology":{"ran":1,"of":2,"n_ran_checked":0,"n_instrument":1,"unverified":1,"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) · 1 unverified","official":{"repos":["imageomics/intr"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"analysis-of-the-user-perception-of-chatbots","title":"Analysis of the User Perception of Chatbots in Education Using A Partial Least Squares Structural Equation Modeling Approach","date":"2023-11-07","arxiv_id":"2311.03636","n_code_links":0,"syntology":null},{"paper":"/paper/black-box-prompt-optimization-aligning-large","slug":"black-box-prompt-optimization-aligning-large","title":"Black-Box Prompt Optimization: Aligning Large Language Models without Model Training","date":"2023-11-07","arxiv_id":"2311.04155","n_code_links":1,"syntology":{"ran":5,"of":7,"n_ran_checked":5,"n_instrument":0,"unverified":2,"pointer_only":0,"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) · 2 unverified","official":{"repos":["thu-coai/bpo"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"evaluating-large-language-models-in","title":"Evaluating Large Language Models in Ophthalmology","date":"2023-11-07","arxiv_id":"2311.04933","n_code_links":0,"syntology":null},{"paper":null,"slug":"evaluating-multiple-large-language-models-in","title":"Evaluating multiple large language models in pediatric ophthalmology","date":"2023-11-07","arxiv_id":"2311.04368","n_code_links":0,"syntology":null},{"paper":null,"slug":"identifying-and-mitigating-vulnerabilities-in","title":"Identifying and Mitigating Vulnerabilities in LLM-Integrated Applications","date":"2023-11-07","arxiv_id":"2311.16153","n_code_links":0,"syntology":null},{"paper":null,"slug":"leveraging-large-language-models-for-3","title":"Leveraging Large Language Models for Automated Proof Synthesis in Rust","date":"2023-11-07","arxiv_id":"2311.03739","n_code_links":0,"syntology":null},{"paper":"/paper/multi-resolution-time-series-transformer-for","slug":"multi-resolution-time-series-transformer-for","title":"Multi-resolution Time-Series Transformer for Long-term Forecasting","date":"2023-11-07","arxiv_id":"2311.04147","n_code_links":2,"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: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["networkslab/mtst","Yitiann/MTST"],"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":"syntax-guided-transformers-elevating","title":"Syntax-Guided Transformers: Elevating Compositional Generalization and Grounding in Multimodal Environments","date":"2023-11-07","arxiv_id":"2311.04364","n_code_links":0,"syntology":null},{"paper":"/paper/towards-garment-sewing-pattern-reconstruction","slug":"towards-garment-sewing-pattern-reconstruction","title":"Towards Garment Sewing Pattern Reconstruction from a Single Image","date":"2023-11-07","arxiv_id":"2311.04218","n_code_links":2,"syntology":null},{"paper":null,"slug":"wearable-data-from-subjects-playing-super","title":"Wearable data from subjects playing Super Mario, sitting university exams, or performing physical exercise help detect acute mood episodes via self-supervised learning","date":"2023-11-07","arxiv_id":"2311.04215","n_code_links":0,"syntology":null},{"paper":"/paper/which-is-better-exploring-prompting-strategy","slug":"which-is-better-exploring-prompting-strategy","title":"Which is better? Exploring Prompting Strategy For LLM-based Metrics","date":"2023-11-07","arxiv_id":"2311.03754","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-simple-yet-efficient-ensemble-approach-for","title":"A Simple yet Efficient Ensemble Approach for AI-generated Text Detection","date":"2023-11-06","arxiv_id":"2311.03084","n_code_links":0,"syntology":null},{"paper":"/paper/can-llms-follow-simple-rules","slug":"can-llms-follow-simple-rules","title":"Can LLMs Follow Simple Rules?","date":"2023-11-06","arxiv_id":"2311.04235","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":["normster/llm_rules"],"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/deepinception-hypnotize-large-language-model","slug":"deepinception-hypnotize-large-language-model","title":"DeepInception: Hypnotize Large Language Model to Be Jailbreaker","date":"2023-11-06","arxiv_id":"2311.03191","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"pointer_only":0,"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":["tmlr-group/deepinception"],"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":"leveraging-transformers-to-improve-breast","title":"Leveraging Transformers to Improve Breast Cancer Classification and Risk Assessment with Multi-modal and Longitudinal Data","date":"2023-11-06","arxiv_id":"2311.03217","n_code_links":0,"syntology":null},{"paper":null,"slug":"machine-learning-based-tea-leaf-disease","title":"Machine Learning-Based Tea Leaf Disease Detection: A Comprehensive Review","date":"2023-11-06","arxiv_id":"2311.03240","n_code_links":0,"syntology":null},{"paper":null,"slug":"nexus-at-araieval-shared-task-fine-tuning","title":"Nexus at ArAIEval Shared Task: Fine-Tuning Arabic Language Models for Propaganda and Disinformation Detection","date":"2023-11-06","arxiv_id":"2311.03184","n_code_links":0,"syntology":null},{"paper":null,"slug":"p-laplacian-transformer","title":"p-Laplacian Transformer","date":"2023-11-06","arxiv_id":"2311.03235","n_code_links":0,"syntology":null},{"paper":null,"slug":"scalable-and-transferable-black-box","title":"Scalable and Transferable Black-Box Jailbreaks for Language Models via Persona Modulation","date":"2023-11-06","arxiv_id":"2311.03348","n_code_links":0,"syntology":null},{"paper":null,"slug":"sugarvit-multi-objective-regression-of-uav","title":"SugarViT -- Multi-objective Regression of UAV Images with Vision Transformers and Deep Label Distribution Learning Demonstrated on Disease Severity Prediction in Sugar Beet","date":"2023-11-06","arxiv_id":"2311.03076","n_code_links":0,"syntology":null},{"paper":"/paper/tsp-transformer-task-specific-prompts-boosted","slug":"tsp-transformer-task-specific-prompts-boosted","title":"TSP-Transformer: Task-Specific Prompts Boosted Transformer for Holistic Scene Understanding","date":"2023-11-06","arxiv_id":"2311.03427","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-critical-perceptual-pre-trained-model-for","title":"A Critical Perceptual Pre-trained Model for Complex Trajectory Recovery","date":"2023-11-05","arxiv_id":"2311.02631","n_code_links":0,"syntology":null},{"paper":null,"slug":"evaluating-the-potential-of-leading-large","title":"Evaluating the Potential of Leading Large Language Models in Reasoning Biology Questions","date":"2023-11-05","arxiv_id":"2311.07582","n_code_links":0,"syntology":null},{"paper":"/paper/exploring-grounding-potential-of-vqa-oriented","slug":"exploring-grounding-potential-of-vqa-oriented","title":"GPT-4V-AD: Exploring Grounding Potential of VQA-oriented GPT-4V for Zero-shot Anomaly Detection","date":"2023-11-05","arxiv_id":"2311.02612","n_code_links":1,"syntology":null},{"paper":null,"slug":"floodbrain-flood-disaster-reporting-by-web","title":"FloodBrain: Flood Disaster Reporting by Web-based Retrieval Augmented Generation with an LLM","date":"2023-11-05","arxiv_id":"2311.02597","n_code_links":0,"syntology":null},{"paper":null,"slug":"large-language-models-implicitly-learn-to","title":"Large language models implicitly learn to straighten neural sentence trajectories to construct a predictive representation of natural language","date":"2023-11-05","arxiv_id":"2311.04930","n_code_links":0,"syntology":null},{"paper":"/paper/mftcoder-boosting-code-llms-with-multitask","slug":"mftcoder-boosting-code-llms-with-multitask","title":"MFTCoder: Boosting Code LLMs with Multitask Fine-Tuning","date":"2023-11-04","arxiv_id":"2311.02303","n_code_links":1,"syntology":null},{"paper":"/paper/scoreperformer-expressive-piano-performance","slug":"scoreperformer-expressive-piano-performance","title":"ScorePerformer: Expressive Piano Performance Rendering With Fine-Grained Control","date":"2023-11-04","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"ultra-long-sequence-distributed-transformer","title":"Ultra-Long Sequence Distributed Transformer","date":"2023-11-04","arxiv_id":"2311.02382","n_code_links":0,"syntology":null}],"record_sha256":"6b3a6f100c2875de0b9adeeeea9b0c7cf3d3f287d104acb8d168fc2316668759","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}