{"total":49,"rows":[{"id":"2210.10723","title":"TabLLM: Few-shot Classification of Tabular Data with Large Language Models","created":"2022-10-19","cited_by_count":277,"area":null},{"id":"2402.17944","title":"Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Survey","created":"2024-02-27","cited_by_count":159,"area":null},{"id":"2308.09718","title":"Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training","created":"2023-08-18","cited_by_count":86,"area":null},{"id":"2211.03044","title":"Tuning Language Models as Training Data Generators for Augmentation-Enhanced Few-Shot Learning","created":"2022-11-06","cited_by_count":36,"area":null},{"id":"2302.12449","title":"SGL-PT: A Strong Graph Learner with Graph Prompt Tuning","created":"2023-02-24","cited_by_count":35,"area":null},{"id":"2405.02743","title":"Beyond Performance: Quantifying and Mitigating Label Bias in LLMs","created":"2024-05-04","cited_by_count":25,"area":null},{"id":"2306.14101","title":"Language models are weak learners","created":"2023-06-25","cited_by_count":22,"area":null},{"id":"2209.11486","title":"MetaPrompting: Learning to Learn Better Prompts","created":"2022-09-23","cited_by_count":19,"area":null},{"id":"2208.08023","title":"Few-shot Named Entity Recognition with Entity-level Prototypical Network Enhanced by Dispersedly Distributed Prototypes","created":"2022-08-17","cited_by_count":17,"area":null},{"id":"2306.00618","title":"Effective Structured Prompting by Meta-Learning and Representative Verbalizer","created":"2023-06-01","cited_by_count":14,"area":null},{"id":"2408.13040","title":"SpeechPrompt: Prompting Speech Language Models for Speech Processing Tasks","created":"2024-08-23","cited_by_count":12,"area":null},{"id":"2210.16637","title":"Beyond Prompting: Making Pre-trained Language Models Better Zero-shot Learners by Clustering Representations","created":"2022-10-29","cited_by_count":11,"area":null},{"id":"2302.09236","title":"Scalable Prompt Generation for Semi-supervised Learning with Language Models","created":"2023-02-18","cited_by_count":11,"area":null},{"id":"2305.16885","title":"Hierarchical Verbalizer for Few-Shot Hierarchical Text Classification","created":"2023-05-26","cited_by_count":11,"area":null},{"id":"2402.07776","title":"TELLER: A Trustworthy Framework for Explainable, Generalizable and Controllable Fake News Detection","created":"2024-02-12","cited_by_count":10,"area":null},{"id":"2305.12576","title":"Automated Few-shot Classification with Instruction-Finetuned Language Models","created":"2023-05-21","cited_by_count":9,"area":null},{"id":"2308.09363","title":"Open-vocabulary Video Question Answering: A New Benchmark for Evaluating the Generalizability of Video Question Answering Models","created":"2023-08-18","cited_by_count":7,"area":null},{"id":"2212.08408","title":"Decoder Tuning: Efficient Language Understanding as Decoding","created":"2022-12-16","cited_by_count":6,"area":null},{"id":"2310.11670","title":"Prototype-based HyperAdapter for Sample-Efficient Multi-task Tuning","created":"2023-10-18","cited_by_count":6,"area":null},{"id":"2305.09892","title":"Clustering-Aware Negative Sampling for Unsupervised Sentence Representation","created":"2023-05-17","cited_by_count":5,"area":null},{"id":"2305.17699","title":"Decoupling Pseudo Label Disambiguation and Representation Learning for Generalized Intent Discovery","created":"2023-05-28","cited_by_count":5,"area":null},{"id":"2306.04101","title":"Gotta: Generative Few-shot Question Answering by Prompt-based Cloze Data Augmentation","created":"2023-06-07","cited_by_count":5,"area":null},{"id":"2303.04998","title":"Rethinking Visual Prompt Learning as Masked Visual Token Modeling","created":"2023-03-09","cited_by_count":4,"area":null},{"id":"2310.13998","title":"Transductive Learning for Textual Few-Shot Classification in API-based Embedding Models","created":"2023-10-21","cited_by_count":4,"area":null},{"id":"2306.10514","title":"Evolutionary Verbalizer Search for Prompt-based Few Shot Text Classification","created":"2023-06-18","cited_by_count":3,"area":null},{"id":"2310.13380","title":"APP: Adaptive Prototypical Pseudo-Labeling for Few-shot OOD Detection","created":"2023-10-20","cited_by_count":3,"area":null},{"id":"2404.11206","title":"Prompt-tuning for Clickbait Detection via Text Summarization","created":"2024-04-17","cited_by_count":3,"area":null},{"id":"2404.19335","title":"StablePT: Towards Stable Prompting for Few-shot Learning via Input Separation","created":"2024-04-30","cited_by_count":3,"area":null},{"id":"2211.05319","title":"Few-shot Classification with Hypersphere Modeling of Prototypes","created":"2022-11-10","cited_by_count":2,"area":null},{"id":"2211.05987","title":"CCPrefix: Counterfactual Contrastive Prefix-Tuning for Many-Class Classification","created":"2022-11-11","cited_by_count":2,"area":null},{"id":"2508.13452","title":"Hierarchy-Consistent Learning and Adaptive Loss Balancing for Hierarchical Multi-Label Classification","created":"2025-08-19","cited_by_count":2,"area":null},{"id":"2511.21038","title":"Semantic Anchors in In-Context Learning: Why Small LLMs Cannot Flip Their Labels","created":"2025-11-26","cited_by_count":2,"area":null},{"id":"2302.07739","title":"Meta-Learning Triplet Network with Adaptive Margins for Few-Shot Named Entity Recognition","created":"2023-02-14","cited_by_count":1,"area":null},{"id":"2304.13639","title":"PVP: Pre-trained Visual Parameter-Efficient Tuning","created":"2023-04-26","cited_by_count":1,"area":null},{"id":"2305.03287","title":"Low-Resource Multi-Granularity Academic Function Recognition Based on Multiple Prompt Knowledge","created":"2023-05-05","cited_by_count":1,"area":null},{"id":"2305.09335","title":"MsPrompt: Multi-step Prompt Learning for Debiasing Few-shot Event Detection","created":"2023-05-16","cited_by_count":1,"area":null},{"id":"2309.04174","title":"Manifold-based Verbalizer Space Re-embedding for Tuning-free Prompt-based Classification","created":"2023-09-08","cited_by_count":1,"area":null},{"id":"2403.03863","title":"X-Shot: A Unified System to Handle Frequent, Few-shot and Zero-shot Learning Simultaneously in Classification","created":"2024-03-06","cited_by_count":1,"area":null},{"id":"2404.02772","title":"FPT: Feature Prompt Tuning for Few-shot Readability Assessment","created":"2024-04-03","cited_by_count":1,"area":null},{"id":"2212.06369","title":"Technical Report -- Competition Solution for Prompt Tuning using Pretrained Language Model","created":"2022-12-13","cited_by_count":0,"area":null},{"id":"2306.08892","title":"MetricPrompt: Prompting Model as a Relevance Metric for Few-shot Text Classification","created":"2023-06-15","cited_by_count":0,"area":null},{"id":"2308.02103","title":"Prompt2Gaussia: Uncertain Prompt-learning for Script Event Prediction","created":"2023-08-04","cited_by_count":0,"area":null},{"id":"2401.05204","title":"A Novel Prompt-tuning Method: Incorporating Scenario-specific Concepts into a Verbalizer","created":"2024-01-10","cited_by_count":0,"area":null},{"id":"2410.06173","title":"Manual Verbalizer Enrichment for Few-Shot Text Classification","created":"2024-10-08","cited_by_count":0,"area":null},{"id":"2410.10454","title":"Improve Meta-learning for Few-Shot Text Classification with All You Can Acquire from the Tasks","created":"2024-10-14","cited_by_count":0,"area":null},{"id":"2410.19195","title":"Label Set Optimization via Activation Distribution Kurtosis for Zero-shot Classification with Generative Models","created":"2024-10-24","cited_by_count":0,"area":null},{"id":"2507.00330","title":"Modeling Data Diversity for Joint Instance and Verbalizer Selection in Cold-Start Scenarios","created":"2025-07-01","cited_by_count":0,"area":null},{"id":"2603.09145","title":"Causally Sufficient and Necessary Feature Expansion for Class-Incremental Learning","created":"2026-03-10","cited_by_count":0,"area":null},{"id":"2604.15998","title":"SCHK-HTC: Sibling Contrastive Learning with Hierarchical Knowledge-Aware Prompt Tuning for Hierarchical Text Classification","created":"2026-04-17","cited_by_count":0,"area":null}],"next_cursor":null}