{"total":30,"rows":[{"id":"2108.01955","title":"Log-based Anomaly Detection Without Log Parsing","created":"2021-08-04","cited_by_count":59,"area":null},{"id":"2106.06297","title":"Dynamic Language Models for Continuously Evolving Content","created":"2021-06-11","cited_by_count":27,"area":null},{"id":"2205.01500","title":"Meta Learning for Natural Language Processing: A Survey","created":"2022-05-03","cited_by_count":26,"area":null},{"id":"2402.12261","title":"NEO-BENCH: Evaluating Robustness of Large Language Models with Neologisms","created":"2024-02-19","cited_by_count":20,"area":null},{"id":"2204.04391","title":"MINER: Improving Out-of-Vocabulary Named Entity Recognition from an Information Theoretic Perspective","created":"2022-04-09","cited_by_count":17,"area":null},{"id":"2105.06725","title":"Meta-Inductive Node Classification across Graphs","created":"2021-05-14","cited_by_count":16,"area":null},{"id":"2207.09980","title":"ReFactor GNNs: Revisiting Factorisation-based Models from a Message-Passing Perspective","created":"2022-07-20","cited_by_count":12,"area":null},{"id":"2203.09308","title":"Few-Shot Learning on Graphs","created":"2022-03-17","cited_by_count":11,"area":null},{"id":"2106.06410","title":"What Can Knowledge Bring to Machine Learning? -- A Survey of Low-shot Learning for Structured Data","created":"2021-06-11","cited_by_count":10,"area":null},{"id":"2203.07860","title":"Imputing Out-of-Vocabulary Embeddings with LOVE Makes Language Models Robust with Little Cost","created":"2022-03-15","cited_by_count":9,"area":null},{"id":"2208.06692","title":"BinBert: Binary Code Understanding with a Fine-tunable and Execution-aware Transformer","created":"2022-08-13","cited_by_count":8,"area":null},{"id":"2201.08455","title":"LOSTIN: Logic Optimization via Spatio-Temporal Information with Hybrid Graph Models","created":"2022-01-20","cited_by_count":7,"area":null},{"id":"2410.15019","title":"A Survey of Ontology Expansion for Conversational Understanding","created":"2024-10-19","cited_by_count":6,"area":null},{"id":"2112.02275","title":"A Multi-Strategy based Pre-Training Method for Cold-Start Recommendation","created":"2021-12-04","cited_by_count":5,"area":null},{"id":"2109.11763","title":"Lacking the embedding of a word? Look it up into a traditional dictionary","created":"2021-09-24","cited_by_count":4,"area":null},{"id":"2502.14791","title":"Rapid Word Learning Through Meta In-Context Learning","created":"2025-02-20","cited_by_count":4,"area":null},{"id":"2306.16326","title":"Representation Learning via Variational Bayesian Networks","created":"2023-06-28","cited_by_count":3,"area":null},{"id":"2403.15362","title":"CoLLEGe: Concept Embedding Generation for Large Language Models","created":"2024-03-22","cited_by_count":3,"area":null},{"id":"2412.08434","title":"Mitigating Out-of-Entity Errors in Named Entity Recognition: A Sentence-Level Strategy","created":"2024-12-11","cited_by_count":3,"area":null},{"id":"2105.14313","title":"Novel Slot Detection: A Benchmark for Discovering Unknown Slot Types in the Task-Oriented Dialogue System","created":"2021-05-29","cited_by_count":2,"area":null},{"id":"2201.07381","title":"Unveiling Project-Specific Bias in Neural Code Models","created":"2022-01-19","cited_by_count":1,"area":null},{"id":"2206.08081","title":"TransDrift: Modeling Word-Embedding Drift using Transformer","created":"2022-06-16","cited_by_count":1,"area":null},{"id":"2305.17127","title":"Characterizing and Measuring Linguistic Dataset Drift","created":"2023-05-26","cited_by_count":1,"area":null},{"id":"2102.12266","title":"Trajectory-Based Meta-Learning for Out-Of-Vocabulary Word Embedding Learning","created":"2021-02-24","cited_by_count":0,"area":null},{"id":"2112.02274","title":"Self-supervised Graph Learning for Occasional Group Recommendation","created":"2021-12-04","cited_by_count":0,"area":null},{"id":"2207.01189","title":"Learning node embeddings via summary graphs: a brief theoretical analysis","created":"2022-07-04","cited_by_count":0,"area":null},{"id":"2406.05307","title":"DeviceBERT: Applied Transfer Learning With Targeted Annotations and Vocabulary Enrichment to Identify Medical Device and Component Terminology in FDA Recall Summaries","created":"2024-06-08","cited_by_count":0,"area":null},{"id":"2504.04017","title":"A Comprehensive Survey of Challenges and Opportunities of Few-Shot Learning Across Multiple Domains","created":"2025-04-05","cited_by_count":0,"area":null},{"id":"2507.04018","title":"Handling Korean Out-of-Vocabulary Words with Phoneme Representation Learning","created":"2025-07-05","cited_by_count":0,"area":null},{"id":"2510.06244","title":"Evaluating Embedding Frameworks for Scientific Domain","created":"2025-10-03","cited_by_count":0,"area":null}],"next_cursor":null}