{"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":"/task/few-shot-learning/papers/18","list_of":"/task/few-shot-learning","task":"Few-Shot Learning","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":18,"pages_in_order":30,"rows_per_page":100,"rows":[1701,1800],"of":2964,"counts":{"archive_papers_tagged":2964,"with_a_code_link":1297,"where_syntology_ran_a_sample":373,"not_listed_spam_title":0,"listed":2964,"listed_where_code_ran":373,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":306,"every_run_a_failure_of_syntologys_instrument":67,"listed_with_a_run_with_no_instrument_failure":306,"listed_every_run_a_failure_of_syntologys_instrument":67,"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":"/task/few-shot-learning","prev":"/task/few-shot-learning/papers/17","next":"/task/few-shot-learning/papers/19","papers":[{"url":null,"slug":"meta-in-context-learning-makes-large-language","title":"Meta In-Context Learning Makes Large Language Models Better Zero and Few-Shot Relation Extractors","date":"2024-04-27","arxiv_id":"2404.17807","repositories_listed":0,"syntology":null},{"url":null,"slug":"empowering-large-language-models-for-textual","title":"Empowering Large Language Models for Textual Data Augmentation","date":"2024-04-26","arxiv_id":"2404.17642","repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-transfer-derm-diagnosis-exploring-few","title":"Meta-Transfer Derm-Diagnosis: Exploring Few-Shot Learning and Transfer Learning for Skin Disease Classification in Long-Tail Distribution","date":"2024-04-25","arxiv_id":"2404.16814","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comprehensive-and-easy-to-use-multi-domain","title":"A comprehensive and easy-to-use multi-domain multi-task medical imaging meta-dataset (MedIMeta)","date":"2024-04-24","arxiv_id":"2404.16000","repositories_listed":0,"syntology":null},{"url":null,"slug":"beyond-deepfake-images-detecting-ai-generated","title":"Beyond Deepfake Images: Detecting AI-Generated Videos","date":"2024-04-24","arxiv_id":"2404.15955","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-machine-learning-in-the-era-of-large","title":"Graph Machine Learning in the Era of Large Language Models (LLMs)","date":"2024-04-23","arxiv_id":"2404.14928","repositories_listed":0,"syntology":null},{"url":null,"slug":"identifying-fairness-issues-in-automatically","title":"Identifying Fairness Issues in Automatically Generated Testing Content","date":"2024-04-23","arxiv_id":"2404.15104","repositories_listed":0,"syntology":null},{"url":null,"slug":"text-dependent-speaker-verification-tdsv","title":"Text-dependent Speaker Verification (TdSV) Challenge 2024: Challenge Evaluation Plan","date":"2024-04-20","arxiv_id":"2404.13428","repositories_listed":0,"syntology":null},{"url":null,"slug":"stance-detection-on-social-media-with-fine","title":"Stance Detection on Social Media with Fine-Tuned Large Language Models","date":"2024-04-18","arxiv_id":"2404.12171","repositories_listed":0,"syntology":null},{"url":null,"slug":"many-shot-in-context-learning","title":"Many-Shot In-Context Learning","date":"2024-04-17","arxiv_id":"2404.11018","repositories_listed":0,"syntology":null},{"url":null,"slug":"cryomae-few-shot-cryo-em-particle-picking","title":"CryoMAE: Few-Shot Cryo-EM Particle Picking with Masked Autoencoders","date":"2024-04-15","arxiv_id":"2404.10178","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-recall-of-large-language-models-a","title":"Improving Recall of Large Language Models: A Model Collaboration Approach for Relational Triple Extraction","date":"2024-04-15","arxiv_id":"2404.09593","repositories_listed":0,"syntology":null},{"url":null,"slug":"gemquad-generating-multilingual-question","title":"GeMQuAD : Generating Multilingual Question Answering Datasets from Large Language Models using Few Shot Learning","date":"2024-04-14","arxiv_id":"2404.09163","repositories_listed":0,"syntology":null},{"url":null,"slug":"pm2-a-new-prompting-multi-modal-model","title":"PM2: A New Prompting Multi-modal Model Paradigm for Few-shot Medical Image Classification","date":"2024-04-13","arxiv_id":"2404.08915","repositories_listed":0,"syntology":null},{"url":null,"slug":"chatgpt-and-general-purpose-ai-count-fruits","title":"ChatGPT and general-purpose AI count fruits in pictures surprisingly well","date":"2024-04-12","arxiv_id":"2404.08515","repositories_listed":0,"syntology":null},{"url":null,"slug":"sketch-plan-generalize-continual-few-shot","title":"Sketch-Plan-Generalize: Learning and Planning with Neuro-Symbolic Programmatic Representations for Inductive Spatial Concepts","date":"2024-04-11","arxiv_id":"2404.07774","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-few-shot-learning-to-classify-primary","title":"Using Few-Shot Learning to Classify Primary Lung Cancer and Other Malignancy with Lung Metastasis in Cytological Imaging via Endobronchial Ultrasound Procedures","date":"2024-04-09","arxiv_id":"2404.06080","repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-object-detection-research-advances","title":"Few-Shot Object Detection: Research Advances and Challenges","date":"2024-04-07","arxiv_id":"2404.04799","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-few-shot-ensemble-learning-with-focal","title":"Robust Few-Shot Ensemble Learning with Focal Diversity-Based Pruning","date":"2024-04-05","arxiv_id":"2404.04434","repositories_listed":0,"syntology":null},{"url":null,"slug":"negatives-make-a-positive-an-embarrassingly","title":"Negatives Make A Positive: An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning","date":"2024-04-04","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"non-negative-subspace-feature-representation","title":"Non-negative Subspace Feature Representation for Few-shot Learning in Medical Imaging","date":"2024-04-03","arxiv_id":"2404.02656","repositories_listed":0,"syntology":null},{"url":null,"slug":"sswsrnet-a-semi-supervised-few-shot-learning","title":"SSwsrNet: A Semi-Supervised Few-Shot Learning Framework for Wireless Signal Recognition","date":"2024-04-03","arxiv_id":"2404.02467","repositories_listed":0,"syntology":null},{"url":null,"slug":"class-incremental-few-shot-event-detection","title":"Class-Incremental Few-Shot Event Detection","date":"2024-04-02","arxiv_id":"2404.01767","repositories_listed":0,"syntology":null},{"url":null,"slug":"is-meta-training-really-necessary-for","title":"Is Meta-training Really Necessary for Molecular Few-Shot Learning ?","date":"2024-04-02","arxiv_id":"2404.02314","repositories_listed":0,"syntology":null},{"url":"/paper/small-language-models-learn-enhanced","slug":"small-language-models-learn-enhanced","title":"Small Language Models Learn Enhanced Reasoning Skills from Medical Textbooks","date":"2024-03-30","arxiv_id":"2404.00376","repositories_listed":0,"syntology":null},{"url":null,"slug":"mfort-qa-multi-hop-few-shot-open-rich-table","title":"MFORT-QA: Multi-hop Few-shot Open Rich Table Question Answering","date":"2024-03-28","arxiv_id":"2403.19116","repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-cross-system-anomaly-trace","title":"Cross-System Categorization of Abnormal Traces in Microservice-Based Systems via Meta-Learning","date":"2024-03-27","arxiv_id":"2403.18998","repositories_listed":0,"syntology":null},{"url":null,"slug":"plot-tal-prompt-learning-with-optimal","title":"PLOT-TAL -- Prompt Learning with Optimal Transport for Few-Shot Temporal Action Localization","date":"2024-03-27","arxiv_id":"2403.18915","repositories_listed":0,"syntology":null},{"url":null,"slug":"real-acoustic-fields-an-audio-visual-room","title":"Real Acoustic Fields: An Audio-Visual Room Acoustics Dataset and Benchmark","date":"2024-03-27","arxiv_id":"2403.18821","repositories_listed":0,"syntology":null},{"url":null,"slug":"automate-knowledge-concept-tagging-on-math","title":"Automate Knowledge Concept Tagging on Math Questions with LLMs","date":"2024-03-26","arxiv_id":"2403.17281","repositories_listed":0,"syntology":null},{"url":null,"slug":"boosting-few-shot-learning-with-disentangled","title":"Boosting Few-Shot Learning with Disentangled Self-Supervised Learning and Meta-Learning for Medical Image Classification","date":"2024-03-26","arxiv_id":"2403.17530","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-domain-knowledge-to-guide-dialog","title":"Using Domain Knowledge to Guide Dialog Structure Induction via Neural Probabilistic Soft Logic","date":"2024-03-26","arxiv_id":"2403.17853","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-the-generalization-of-cancer","title":"Exploring the Generalization of Cancer Clinical Trial Eligibility Classifiers Across Diseases","date":"2024-03-25","arxiv_id":"2403.17135","repositories_listed":0,"syntology":null},{"url":null,"slug":"segicl-a-universal-in-context-learning","title":"SegICL: A Multimodal In-context Learning Framework for Enhanced Segmentation in Medical Imaging","date":"2024-03-25","arxiv_id":"2403.16578","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-little-leak-will-sink-a-great-ship-survey","title":"A Little Leak Will Sink a Great Ship: Survey of Transparency for Large Language Models from Start to Finish","date":"2024-03-24","arxiv_id":"2403.16139","repositories_listed":0,"syntology":null},{"url":null,"slug":"boosting-few-shot-learning-via-attentive","title":"Boosting Few-Shot Learning via Attentive Feature Regularization","date":"2024-03-23","arxiv_id":"2403.17025","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-learn-the-wave-angle-estimation","title":"Learning-to-Learn the Wave Angle Estimation","date":"2024-03-21","arxiv_id":"2403.14306","repositories_listed":0,"syntology":null},{"url":null,"slug":"clinical-information-extraction-for-low","title":"Clinical information extraction for Low-resource languages with Few-shot learning using Pre-trained language models and Prompting","date":"2024-03-20","arxiv_id":"2403.13369","repositories_listed":0,"syntology":null},{"url":null,"slug":"luwa-dataset-learning-lithic-use-wear","title":"LUWA Dataset: Learning Lithic Use-Wear Analysis on Microscopic Images","date":"2024-03-19","arxiv_id":"2403.13171","repositories_listed":0,"syntology":null},{"url":null,"slug":"better-pseudo-labels-for-semi-supervised","title":"Better (pseudo-)labels for semi-supervised instance segmentation","date":"2024-03-18","arxiv_id":"2403.11675","repositories_listed":0,"syntology":null},{"url":null,"slug":"co3-low-resource-contrastive-co-training-for","title":"CO3: Low-resource Contrastive Co-training for Generative Conversational Query Rewrite","date":"2024-03-18","arxiv_id":"2403.11873","repositories_listed":0,"syntology":null},{"url":null,"slug":"parmesan-parameter-free-memory-search-and","title":"PARMESAN: Parameter-Free Memory Search and Transduction for Dense Prediction Tasks","date":"2024-03-18","arxiv_id":"2403.11743","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-understanding-the-relationship","title":"Towards Understanding the Relationship between In-context Learning and Compositional Generalization","date":"2024-03-18","arxiv_id":"2403.11834","repositories_listed":0,"syntology":null},{"url":null,"slug":"mixture-of-prompt-experts-for-multi-modal","title":"Mixture-of-Prompt-Experts for Multi-modal Semantic Understanding","date":"2024-03-17","arxiv_id":"2403.11311","repositories_listed":0,"syntology":null},{"url":null,"slug":"investigating-grammatical-abstraction-in","title":"Investigating grammatical abstraction in language models using few-shot learning of novel noun gender","date":"2024-03-15","arxiv_id":"2403.10338","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-low-shot-transferability-of-v-mamba","title":"On the low-shot transferability of [V]-Mamba","date":"2024-03-15","arxiv_id":"2403.10696","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-objective-optimization-using-adaptive","title":"Multi-Objective Optimization Using Adaptive Distributed Reinforcement Learning","date":"2024-03-13","arxiv_id":"2403.08879","repositories_listed":0,"syntology":null},{"url":null,"slug":"search-based-optimisation-of-llm-learning","title":"Search-based Optimisation of LLM Learning Shots for Story Point Estimation","date":"2024-03-13","arxiv_id":"2403.08430","repositories_listed":0,"syntology":null},{"url":null,"slug":"segmentation-of-knee-bones-for-osteoarthritis","title":"Segmentation of Knee Bones for Osteoarthritis Assessment: A Comparative Analysis of Supervised, Few-Shot, and Zero-Shot Learning Approaches","date":"2024-03-13","arxiv_id":"2403.08761","repositories_listed":0,"syntology":null},{"url":null,"slug":"agile-gesture-recognition-for-low-power","title":"Agile gesture recognition for low-power applications: customisation for generalisation","date":"2024-03-12","arxiv_id":"2403.15421","repositories_listed":0,"syntology":null},{"url":null,"slug":"mentor-multilingual-text-detection-toward","title":"MENTOR: Multilingual tExt detectioN TOward leaRning by analogy","date":"2024-03-12","arxiv_id":"2403.07286","repositories_listed":0,"syntology":null},{"url":null,"slug":"rethinking-aste-a-minimalist-tagging-scheme","title":"Rethinking ASTE: A Minimalist Tagging Scheme Alongside Contrastive Learning","date":"2024-03-12","arxiv_id":"2403.07342","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-the-energy-efficiency-of-few-shot","title":"Evaluating the Energy Efficiency of Few-Shot Learning for Object Detection in Industrial Settings","date":"2024-03-11","arxiv_id":"2403.06631","repositories_listed":0,"syntology":null},{"url":null,"slug":"fedpit-towards-privacy-preserving-and-few","title":"FewFedPIT: Towards Privacy-preserving and Few-shot Federated Instruction Tuning","date":"2024-03-10","arxiv_id":"2403.06131","repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-learning-on-heterogeneous-graphs","title":"Few-shot Learning on Heterogeneous Graphs: Challenges, Progress, and Prospects","date":"2024-03-10","arxiv_id":"2403.13834","repositories_listed":0,"syntology":null},{"url":null,"slug":"contrastive-augmented-graph2graph-memory","title":"Contrastive Augmented Graph2Graph Memory Interaction for Few Shot Continual Learning","date":"2024-03-07","arxiv_id":"2403.04140","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-icl-definition-enriched-experts-for","title":"TEGEE: Task dEfinition Guided Expert Ensembling for Generalizable and Few-shot Learning","date":"2024-03-07","arxiv_id":"2403.04233","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-aware-relational-graph-neural","title":"Uncertainty-Aware Relational Graph Neural Network for Few-Shot Knowledge Graph Completion","date":"2024-03-07","arxiv_id":"2403.04521","repositories_listed":0,"syntology":null},{"url":null,"slug":"boosting-meta-training-with-base-class","title":"Boosting Meta-Training with Base Class Information for Few-Shot Learning","date":"2024-03-06","arxiv_id":"2403.03472","repositories_listed":0,"syntology":null},{"url":null,"slug":"designing-informative-metrics-for-few-shot","title":"Designing Informative Metrics for Few-Shot Example Selection","date":"2024-03-06","arxiv_id":"2403.03861","repositories_listed":0,"syntology":null},{"url":null,"slug":"japanese-english-sentence-translation","title":"Japanese-English Sentence Translation Exercises Dataset for Automatic Grading","date":"2024-03-06","arxiv_id":"2403.03396","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-transfer-in-classification-how-well-do","title":"On Transfer in Classification: How Well do Subsets of Classes Generalize?","date":"2024-03-06","arxiv_id":"2403.03569","repositories_listed":0,"syntology":null},{"url":null,"slug":"zero-shot-cross-lingual-document-level-event","title":"Zero-Shot Cross-Lingual Document-Level Event Causality Identification with Heterogeneous Graph Contrastive Transfer Learning","date":"2024-03-05","arxiv_id":"2403.02893","repositories_listed":0,"syntology":null},{"url":null,"slug":"analyzing-and-adapting-large-language-models","title":"Analyzing and Adapting Large Language Models for Few-Shot Multilingual NLU: Are We There Yet?","date":"2024-03-04","arxiv_id":"2403.01929","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-does-architecture-influence-the-base","title":"How does Architecture Influence the Base Capabilities of Pre-trained Language Models? A Case Study Based on FFN-Wider and MoE Transformers","date":"2024-03-04","arxiv_id":"2403.02436","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-weakly-annotated-data-for-hate","title":"Leveraging Weakly Annotated Data for Hate Speech Detection in Code-Mixed Hinglish: A Feasibility-Driven Transfer Learning Approach with Large Language Models","date":"2024-03-04","arxiv_id":"2403.02121","repositories_listed":0,"syntology":null},{"url":null,"slug":"tpllm-a-traffic-prediction-framework-based-on","title":"TPLLM: A Traffic Prediction Framework Based on Pretrained Large Language Models","date":"2024-03-04","arxiv_id":"2403.02221","repositories_listed":0,"syntology":null},{"url":null,"slug":"transformers-for-supervised-online-continual","title":"Transformers for Supervised Online Continual Learning","date":"2024-03-03","arxiv_id":"2403.01554","repositories_listed":0,"syntology":null},{"url":null,"slug":"forml-a-riemannian-hessian-free-method-for","title":"FORML: A Riemannian Hessian-free Method for Meta-learning on Stiefel Manifolds","date":"2024-02-28","arxiv_id":"2402.18605","repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-tasks-an-alternative-view-on-meta","title":"Meta-Tasks: An alternative view on Meta-Learning Regularization","date":"2024-02-27","arxiv_id":"2402.18599","repositories_listed":0,"syntology":null},{"url":null,"slug":"surgment-segmentation-enabled-semantic-search","title":"Surgment: Segmentation-enabled Semantic Search and Creation of Visual Question and Feedback to Support Video-Based Surgery Learning","date":"2024-02-27","arxiv_id":"2402.17903","repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-learning-for-annotation-efficient","title":"Few-Shot Learning for Annotation-Efficient Nucleus Instance Segmentation","date":"2024-02-26","arxiv_id":"2402.16280","repositories_listed":0,"syntology":null},{"url":null,"slug":"intelligent-known-and-novel-aircraft","title":"Intelligent Known and Novel Aircraft Recognition -- A Shift from Classification to Similarity Learning for Combat Identification","date":"2024-02-26","arxiv_id":"2402.16486","repositories_listed":0,"syntology":null},{"url":null,"slug":"dental-severity-assessment-through-few-shot","title":"Dental Severity Assessment through Few-shot Learning and SBERT Fine-tuning","date":"2024-02-24","arxiv_id":"2402.15755","repositories_listed":0,"syntology":null},{"url":null,"slug":"trec-apt-tactic-technique-recognition-via-few","title":"TREC: APT Tactic / Technique Recognition via Few-Shot Provenance Subgraph Learning","date":"2024-02-23","arxiv_id":"2402.15147","repositories_listed":0,"syntology":null},{"url":null,"slug":"clce-an-approach-to-refining-cross-entropy","title":"CLCE: An Approach to Refining Cross-Entropy and Contrastive Learning for Optimized Learning Fusion","date":"2024-02-22","arxiv_id":"2402.14551","repositories_listed":0,"syntology":null},{"url":null,"slug":"small-language-model-is-a-good-guide-for","title":"Small Language Models as Effective Guides for Large Language Models in Chinese Relation Extraction","date":"2024-02-22","arxiv_id":"2402.14373","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-important-is-domain-specificity-in","title":"How Important is Domain Specificity in Language Models and Instruction Finetuning for Biomedical Relation Extraction?","date":"2024-02-21","arxiv_id":"2402.13470","repositories_listed":0,"syntology":null},{"url":null,"slug":"vl-trojan-multimodal-instruction-backdoor","title":"VL-Trojan: Multimodal Instruction Backdoor Attacks against Autoregressive Visual Language Models","date":"2024-02-21","arxiv_id":"2402.13851","repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-clinical-entity-recognition-in-three","title":"Few-shot clinical entity recognition in English, French and Spanish: masked language models outperform generative model prompting","date":"2024-02-20","arxiv_id":"2402.12801","repositories_listed":0,"syntology":null},{"url":null,"slug":"opdai-at-semeval-2024-task-6-small-llms-can","title":"OPDAI at SemEval-2024 Task 6: Small LLMs can Accelerate Hallucination Detection with Weakly Supervised Data","date":"2024-02-20","arxiv_id":"2402.12913","repositories_listed":0,"syntology":null},{"url":null,"slug":"deepcode-ai-fix-fixing-security","title":"DeepCode AI Fix: Fixing Security Vulnerabilities with Large Language Models","date":"2024-02-19","arxiv_id":"2402.13291","repositories_listed":0,"syntology":null},{"url":null,"slug":"prompt-based-bias-calibration-for-better-zero","title":"Prompt-Based Bias Calibration for Better Zero/Few-Shot Learning of Language Models","date":"2024-02-15","arxiv_id":"2402.10353","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-secure-are-large-language-models-llms-for","title":"How Secure Are Large Language Models (LLMs) for Navigation in Urban Environments?","date":"2024-02-14","arxiv_id":"2402.09546","repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-learning-with-uncertainty-based","title":"Few-Shot Learning with Uncertainty-based Quadruplet Selection for Interference Classification in GNSS Data","date":"2024-02-09","arxiv_id":"2402.09466","repositories_listed":0,"syntology":null},{"url":null,"slug":"revealing-multimodal-contrastive","title":"Beyond DAGs: A Latent Partial Causal Model for Multimodal Learning","date":"2024-02-09","arxiv_id":"2402.06223","repositories_listed":0,"syntology":null},{"url":null,"slug":"advancing-anomaly-detection-an-adaptation","title":"Advancing Video Anomaly Detection: A Concise Review and a New Dataset","date":"2024-02-07","arxiv_id":"2402.04857","repositories_listed":0,"syntology":null},{"url":null,"slug":"l4q-parameter-efficient-quantization-aware","title":"L4Q: Parameter Efficient Quantization-Aware Fine-Tuning on Large Language Models","date":"2024-02-07","arxiv_id":"2402.04902","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-low-resource-medical-image","title":"Exploring Low-Resource Medical Image Classification with Weakly Supervised Prompt Learning","date":"2024-02-06","arxiv_id":"2402.03783","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-combination-of-sample-selection","title":"Automatic Combination of Sample Selection Strategies for Few-Shot Learning","date":"2024-02-05","arxiv_id":"2402.03038","repositories_listed":0,"syntology":null},{"url":null,"slug":"toward-green-and-human-like-artificial","title":"A Complete Survey on Contemporary Methods, Emerging Paradigms and Hybrid Approaches for Few-Shot Learning","date":"2024-02-05","arxiv_id":"2402.03017","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-the-efficiency-of-protein-language","title":"Enhancing the efficiency of protein language models with minimal wet-lab data through few-shot learning","date":"2024-02-03","arxiv_id":"2402.02004","repositories_listed":0,"syntology":null},{"url":null,"slug":"synthdst-synthetic-data-is-all-you-need-for","title":"SynthDST: Synthetic Data is All You Need for Few-Shot Dialog State Tracking","date":"2024-02-03","arxiv_id":"2402.02285","repositories_listed":0,"syntology":null},{"url":null,"slug":"eeg-gpt-exploring-capabilities-of-large","title":"EEG-GPT: Exploring Capabilities of Large Language Models for EEG Classification and Interpretation","date":"2024-01-31","arxiv_id":"2401.18006","repositories_listed":0,"syntology":null},{"url":null,"slug":"episodic-free-task-selection-for-few-shot","title":"Episodic-free Task Selection for Few-shot Learning","date":"2024-01-31","arxiv_id":"2402.00092","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-random-to-informed-data-selection-a","title":"From Random to Informed Data Selection: A Diversity-Based Approach to Optimize Human Annotation and Few-Shot Learning","date":"2024-01-24","arxiv_id":"2401.13229","repositories_listed":0,"syntology":null},{"url":null,"slug":"growing-from-exploration-a-self-exploring","title":"Growing from Exploration: A self-exploring framework for robots based on foundation models","date":"2024-01-24","arxiv_id":"2401.13462","repositories_listed":0,"syntology":null},{"url":null,"slug":"it-s-about-time-incorporating-temporality-in","title":"It's About Time: Incorporating Temporality in Retrieval Augmented Language Models","date":"2024-01-24","arxiv_id":"2401.13222","repositories_listed":0,"syntology":null},{"url":null,"slug":"ldca-local-descriptors-with-contextual","title":"LDCA: Local Descriptors with Contextual Augmentation for Few-Shot Learning","date":"2024-01-24","arxiv_id":"2401.13499","repositories_listed":0,"syntology":null},{"url":null,"slug":"training-microrobots-to-swim-by-a-large","title":"Training microrobots to swim by a large language model","date":"2024-01-21","arxiv_id":"2402.00044","repositories_listed":0,"syntology":null}],"record_sha256":"158ec22d5746e5fba6c0008f2119addc617eb8d877243ce295711157a32d21ef","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}