{"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/attention/papers/172","list_of":"/method/attention","method":"Attention","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":172,"pages_in_order":316,"rows_per_page":100,"rows":[17101,17200],"of":31583,"counts":{"archive_papers_tagged":31583,"with_a_code_link":13473,"where_syntology_ran_a_sample":3998,"not_listed_spam_title":0,"listed":31583,"listed_where_code_ran":3998,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":3366,"every_run_a_failure_of_syntologys_instrument":632,"listed_with_a_run_with_no_instrument_failure":3366,"listed_every_run_a_failure_of_syntologys_instrument":632,"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/attention","prev":"/method/attention/papers/171","next":"/method/attention/papers/173","papers":[{"paper":"/paper/artificial-text-boundary-detection-with","slug":"artificial-text-boundary-detection-with","title":"AI-generated text boundary detection with RoFT","date":"2023-11-14","arxiv_id":"2311.08349","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":["silversolver/ai_boundary_detection"],"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/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":"cpopqa-ranking-cultural-concept-popularity-by","title":"CPopQA: Ranking Cultural Concept Popularity by LLMs","date":"2023-11-14","arxiv_id":"2311.07897","n_code_links":0,"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/fair-abstractive-summarization-of-diverse","slug":"fair-abstractive-summarization-of-diverse","title":"Fair Abstractive Summarization of Diverse Perspectives","date":"2023-11-14","arxiv_id":"2311.07884","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":null,"slug":"investigating-the-encoding-of-words-in-bert-s","title":"Investigating the Encoding of Words in BERT's Neurons using Feature Textualization","date":"2023-11-14","arxiv_id":"2311.08240","n_code_links":0,"syntology":null},{"paper":"/paper/language-models-are-better-bug-detector","slug":"language-models-are-better-bug-detector","title":"Language Models are Better Bug Detector Through Code-Pair Classification","date":"2023-11-14","arxiv_id":"2311.07957","n_code_links":1,"syntology":null},{"paper":null,"slug":"large-language-model-driven-classroom","title":"Large Language Model-Driven Classroom Flipping: Empowering Student-Centric Peer Questioning with Flipped Interaction","date":"2023-11-14","arxiv_id":"2311.14708","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":"/paper/memory-efficient-stochastic-methods-for","slug":"memory-efficient-stochastic-methods-for","title":"Memory-efficient Stochastic methods for Memory-based Transformers","date":"2023-11-14","arxiv_id":"2311.08123","n_code_links":1,"syntology":null},{"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":"/paper/spot-a-natural-language-interface-for","slug":"spot-a-natural-language-interface-for","title":"Spot: A Natural Language Interface for Geospatial Searches in OSM","date":"2023-11-14","arxiv_id":"2311.08093","n_code_links":1,"syntology":null},{"paper":null,"slug":"ut5-pretraining-non-autoregressive-t5-with","title":"UT5: Pretraining Non autoregressive T5 with unrolled denoising","date":"2023-11-14","arxiv_id":"2311.08552","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/do-large-language-models-and-humans-have","slug":"do-large-language-models-and-humans-have","title":"Do large language models and humans have similar behaviors in causal inference with script knowledge?","date":"2023-11-13","arxiv_id":"2311.07311","n_code_links":1,"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":"/paper/in-context-learning-generalizes-but-not","slug":"in-context-learning-generalizes-but-not","title":"In-context Learning Generalizes, But Not Always Robustly: The Case of Syntax","date":"2023-11-13","arxiv_id":"2311.07811","n_code_links":1,"syntology":null},{"paper":"/paper/interaction-is-all-you-need-a-study-of-robots","slug":"interaction-is-all-you-need-a-study-of-robots","title":"Interaction is all You Need? A Study of Robots Ability to Understand and Execute","date":"2023-11-13","arxiv_id":"2311.07150","n_code_links":1,"syntology":null},{"paper":"/paper/it-s-not-easy-being-wrong-evaluating-process","slug":"it-s-not-easy-being-wrong-evaluating-process","title":"It's Not Easy Being Wrong: Large Language Models Struggle with Process of Elimination Reasoning","date":"2023-11-13","arxiv_id":"2311.07532","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":"language-model-in-the-loop-data-optimal","title":"Language Model-In-The-Loop: Data Optimal Approach to Learn-To-Recommend Actions in Text Games","date":"2023-11-13","arxiv_id":"2311.07687","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":"on-the-truthfulness-of-surprisingly-likely","title":"On The Truthfulness of 'Surprisingly Likely' Responses of Large Language Models","date":"2023-11-13","arxiv_id":"2311.07692","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":"/paper/steer-unified-style-transfer-with-expert","slug":"steer-unified-style-transfer-with-expert","title":"STEER: Unified Style Transfer with Expert Reinforcement","date":"2023-11-13","arxiv_id":"2311.07167","n_code_links":1,"syntology":null},{"paper":null,"slug":"teach-me-with-a-whisper-enhancing-large","title":"Teach me with a Whisper: Enhancing Large Language Models for Analyzing Spoken Transcripts using Speech Embeddings","date":"2023-11-13","arxiv_id":"2311.07014","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":"from-complex-to-simple-unraveling-the","title":"From Complex to Simple: Unraveling the Cognitive Tree for Reasoning with Small Language Models","date":"2023-11-12","arxiv_id":"2311.06754","n_code_links":0,"syntology":null},{"paper":null,"slug":"giellm-japanese-general-information","title":"GIELLM: Japanese General Information Extraction Large Language Model Utilizing Mutual Reinforcement Effect","date":"2023-11-12","arxiv_id":"2311.06838","n_code_links":0,"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":"retrieval-and-generative-approaches-for-a","title":"Retrieval and Generative Approaches for a Pregnancy Chatbot in Nepali with Stemmed and Non-Stemmed Data : A Comparative Study","date":"2023-11-12","arxiv_id":"2311.06898","n_code_links":0,"syntology":null},{"paper":null,"slug":"self-explain-teaching-large-language-models","title":"Large Language Models are In-context Teachers for Knowledge Reasoning","date":"2023-11-12","arxiv_id":"2311.06985","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":null,"slug":"argumentation-element-annotation-modeling","title":"Argumentation Element Annotation Modeling using XLNet","date":"2023-11-10","arxiv_id":"2311.06239","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":"establishing-performance-baselines-in-fine","title":"Establishing Performance Baselines in Fine-Tuning, Retrieval-Augmented Generation and Soft-Prompting for Non-Specialist LLM Users","date":"2023-11-10","arxiv_id":"2311.05903","n_code_links":0,"syntology":null},{"paper":null,"slug":"exploring-fine-tuning-chatgpt-for-news","title":"Exploring Fine-tuning ChatGPT for News Recommendation","date":"2023-11-10","arxiv_id":"2311.05850","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/smart-agent-based-modeling-on-the-use-of","slug":"smart-agent-based-modeling-on-the-use-of","title":"Smart Agent-Based Modeling: On the Use of Large Language Models in Computer Simulations","date":"2023-11-10","arxiv_id":"2311.06330","n_code_links":4,"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":["roihn/sabm"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/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":"/paper/deep-natural-language-feature-learning-for","slug":"deep-natural-language-feature-learning-for","title":"Deep Natural Language Feature Learning for Interpretable Prediction","date":"2023-11-09","arxiv_id":"2311.05754","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":"geoformer-predicting-human-mobility-using","title":"GeoFormer: Predicting Human Mobility using Generative Pre-trained Transformer (GPT)","date":"2023-11-09","arxiv_id":"2311.05092","n_code_links":0,"syntology":null},{"paper":"/paper/glioblastoma-tumor-segmentation-using-an","slug":"glioblastoma-tumor-segmentation-using-an","title":"Glioblastoma Tumor Segmentation using an Ensemble of Vision Transformers","date":"2023-11-09","arxiv_id":"2312.11467","n_code_links":1,"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":"large-language-models-and-prompt-engineering","title":"Large Language Models and Prompt Engineering for Biomedical Query Focused Multi-Document Summarisation","date":"2023-11-09","arxiv_id":"2311.05169","n_code_links":0,"syntology":null},{"paper":null,"slug":"leveraging-artificial-intelligence-technology","title":"Leveraging Artificial Intelligence Technology for Mapping Research to Sustainable Development Goals: A Case Study","date":"2023-11-09","arxiv_id":"2311.16162","n_code_links":0,"syntology":null},{"paper":null,"slug":"logshield-a-transformer-based-apt-detection","title":"LogShield: A Transformer-based APT Detection System Leveraging Self-Attention","date":"2023-11-09","arxiv_id":"2311.05733","n_code_links":0,"syntology":null},{"paper":"/paper/lumos-learning-agents-with-unified-data","slug":"lumos-learning-agents-with-unified-data","title":"Agent Lumos: Unified and Modular Training for Open-Source Language Agents","date":"2023-11-09","arxiv_id":"2311.05657","n_code_links":2,"syntology":{"ran":2,"of":3,"n_ran_checked":0,"n_instrument":2,"unverified":1,"pointer_only":0,"phrase":"2 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; 2 where Syntology's instrument failed) · 1 unverified","official":{"repos":["allenai/lumos"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"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":null,"slug":"dacbert-leveraging-dependency-agreement-for","title":"DACBERT: Leveraging Dependency Agreement for Cost-Efficient Bert Pretraining","date":"2023-11-08","arxiv_id":"2311.04799","n_code_links":0,"syntology":null},{"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/deep-learning-brasil-at-absapt-2022","slug":"deep-learning-brasil-at-absapt-2022","title":"Deep Learning Brasil at ABSAPT 2022: Portuguese Transformer Ensemble Approaches","date":"2023-11-08","arxiv_id":"2311.05051","n_code_links":1,"syntology":null},{"paper":"/paper/deeplearningbrasil-lt-edi-2023-exploring-deep","slug":"deeplearningbrasil-lt-edi-2023-exploring-deep","title":"DeepLearningBrasil@LT-EDI-2023: Exploring Deep Learning Techniques for Detecting Depression in Social Media Text","date":"2023-11-08","arxiv_id":"2311.05047","n_code_links":1,"syntology":null},{"paper":"/paper/determination-of-toxic-comments-and","slug":"determination-of-toxic-comments-and","title":"Determination of toxic comments and unintended model bias minimization using Deep learning approach","date":"2023-11-08","arxiv_id":"2311.04789","n_code_links":1,"syntology":null},{"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/massive-editing-for-large-language-models-via","slug":"massive-editing-for-large-language-models-via","title":"Massive Editing for Large Language Models via Meta Learning","date":"2023-11-08","arxiv_id":"2311.04661","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, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["chenmientan/malmen"],"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"]}}},{"paper":"/paper/nlqxform-a-language-model-based-question-to","slug":"nlqxform-a-language-model-based-question-to","title":"NLQxform: A Language Model-based Question to SPARQL Transformer","date":"2023-11-08","arxiv_id":"2311.07588","n_code_links":1,"syntology":null},{"paper":null,"slug":"pre-training-llms-using-human-like","title":"Pre-training LLMs using human-like development data corpus","date":"2023-11-08","arxiv_id":"2311.04666","n_code_links":0,"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}],"record_sha256":"f3230150bd843e3d596ae4fabed964701801ae5ad44c01106e2e72e479ada392","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}