{"total":137,"rows":[{"id":"2109.01903","title":"Robust fine-tuning of zero-shot models","created":"2021-09-04","cited_by_count":797,"area":null},{"id":"2202.10054","title":"Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution","created":"2022-02-21","cited_by_count":739,"area":null},{"id":"2308.01263","title":"XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models","created":"2023-08-02","cited_by_count":505,"area":null},{"id":"2205.05131","title":"UL2: Unifying Language Learning Paradigms","created":"2022-05-10","cited_by_count":295,"area":null},{"id":"2205.10770","title":"Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models","created":"2022-05-22","cited_by_count":220,"area":null},{"id":"2210.11466","title":"Surgical Fine-Tuning Improves Adaptation to Distribution Shifts","created":"2022-10-20","cited_by_count":205,"area":null},{"id":"2106.14448","title":"R-Drop: Regularized Dropout for Neural Networks","created":"2021-06-28","cited_by_count":202,"area":null},{"id":"2101.11038","title":"Muppet: Massive Multi-task Representations with Pre-Finetuning","created":"2021-01-26","cited_by_count":192,"area":null},{"id":"2402.18013","title":"A Survey on Recent Advances in LLM-Based Multi-turn Dialogue Systems","created":"2024-02-28","cited_by_count":170,"area":null},{"id":"2106.03164","title":"On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation","created":"2021-06-06","cited_by_count":147,"area":null},{"id":"2109.05687","title":"Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning","created":"2021-09-13","cited_by_count":146,"area":null},{"id":"2201.07520","title":"CM3: A Causal Masked Multimodal Model of the Internet","created":"2022-01-19","cited_by_count":142,"area":null},{"id":"2108.05036","title":"DEMix Layers: Disentangling Domains for Modular Language Modeling","created":"2021-08-11","cited_by_count":132,"area":null},{"id":"2102.08473","title":"COCO-LM: Correcting and Contrasting Text Sequences for Language Model Pretraining","created":"2021-02-16","cited_by_count":126,"area":null},{"id":"2210.00045","title":"Calibrating Sequence likelihood Improves Conditional Language Generation","created":"2022-09-30","cited_by_count":121,"area":null},{"id":"2110.08529","title":"Sharpness-Aware Minimization Improves Language Model Generalization","created":"2021-10-16","cited_by_count":108,"area":null},{"id":"2106.07345","title":"Self-Guided Contrastive Learning for BERT Sentence Representations","created":"2021-06-03","cited_by_count":103,"area":null},{"id":"2210.07321","title":"Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods","created":"2022-10-13","cited_by_count":79,"area":null},{"id":"2202.00661","title":"When Do Flat Minima Optimizers Work?","created":"2022-02-01","cited_by_count":78,"area":null},{"id":"2107.06955","title":"HTLM: Hyper-Text Pre-Training and Prompting of Language Models","created":"2021-07-14","cited_by_count":58,"area":null},{"id":"2406.11289","title":"A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Models","created":"2024-06-17","cited_by_count":52,"area":null},{"id":"2502.04194","title":"The Best Instruction-Tuning Data are Those That Fit","created":"2025-02-06","cited_by_count":52,"area":null},{"id":"2404.01260","title":"Bridging Remote Sensors with Multisensor Geospatial Foundation Models","created":"2024-04-01","cited_by_count":46,"area":null},{"id":"2202.12024","title":"NoisyTune: A Little Noise Can Help You Finetune Pretrained Language Models Better","created":"2022-02-24","cited_by_count":43,"area":null},{"id":"2106.08226","title":"Consistency Regularization for Cross-Lingual Fine-Tuning","created":"2021-06-15","cited_by_count":41,"area":null},{"id":"2410.17146","title":"LiNeS: Post-training Layer Scaling Prevents Forgetting and Enhances Model Merging","created":"2024-10-22","cited_by_count":41,"area":null},{"id":"2111.12292","title":"Improved Fine-Tuning by Better Leveraging Pre-Training Data","created":"2021-11-24","cited_by_count":36,"area":null},{"id":"2302.05738","title":"Cross-Modal Fine-Tuning: Align then Refine","created":"2023-02-11","cited_by_count":34,"area":null},{"id":"2106.00149","title":"HiddenCut: Simple Data Augmentation for Natural Language Understanding with Better Generalization","created":"2021-05-31","cited_by_count":32,"area":null},{"id":"2109.03481","title":"Sequence Level Contrastive Learning for Text Summarization","created":"2021-09-08","cited_by_count":31,"area":null},{"id":"2107.04835","title":"Noise Stability Regularization for Improving BERT Fine-tuning","created":"2021-07-10","cited_by_count":29,"area":null},{"id":"2310.04400","title":"On the Embedding Collapse when Scaling up Recommendation Models","created":"2023-10-06","cited_by_count":29,"area":null},{"id":"2210.12517","title":"Exploring The Landscape of Distributional Robustness for Question Answering Models","created":"2022-10-22","cited_by_count":23,"area":null},{"id":"2211.09359","title":"How to Fine-Tune Vision Models with SGD","created":"2022-11-17","cited_by_count":23,"area":null},{"id":"2308.02533","title":"Improving Generalization of Adversarial Training via Robust Critical Fine-Tuning","created":"2023-08-01","cited_by_count":23,"area":null},{"id":"2107.00176","title":"Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards","created":"2021-07-01","cited_by_count":22,"area":null},{"id":"2209.10052","title":"Adapting Pretrained Text-to-Text Models for Long Text Sequences","created":"2022-09-21","cited_by_count":21,"area":null},{"id":"2403.10801","title":"Securely Fine-tuning Pre-trained Encoders Against Adversarial Examples","created":"2024-03-16","cited_by_count":21,"area":null},{"id":"2312.02798","title":"Weakly Supervised Detection of Hallucinations in LLM Activations","created":"2023-12-05","cited_by_count":20,"area":null},{"id":"2112.07198","title":"From Dense to Sparse: Contrastive Pruning for Better Pre-trained Language Model Compression","created":"2021-12-14","cited_by_count":19,"area":null},{"id":"2302.05779","title":"How to prepare your task head for finetuning","created":"2023-02-11","cited_by_count":19,"area":null},{"id":"2104.07275","title":"Span Pointer Networks for Non-Autoregressive Task-Oriented Semantic Parsing","created":"2021-04-15","cited_by_count":18,"area":null},{"id":"2211.01642","title":"Fine-Tuning Pre-Trained Language Models Effectively by Optimizing Subnetworks Adaptively","created":"2022-11-03","cited_by_count":18,"area":null},{"id":"2211.16349","title":"BARTSmiles: Generative Masked Language Models for Molecular Representations","created":"2022-11-29","cited_by_count":18,"area":null},{"id":"2510.00526","title":"Beyond Log Likelihood: Probability-Based Objectives for Supervised Fine-Tuning across the Model Capability Continuum","created":"2025-10-01","cited_by_count":18,"area":null},{"id":"2204.11117","title":"Exploring the Role of Task Transferability in Large-Scale Multi-Task Learning","created":"2022-04-23","cited_by_count":17,"area":null},{"id":"2112.10991","title":"Regularizing End-to-End Speech Translation with Triangular Decomposition Agreement","created":"2021-12-21","cited_by_count":16,"area":null},{"id":"2308.08796","title":"Chinese Spelling Correction as Rephrasing Language Model","created":"2023-08-17","cited_by_count":15,"area":null},{"id":"2306.10790","title":"Preserving Commonsense Knowledge from Pre-trained Language Models via Causal Inference","created":"2023-06-19","cited_by_count":13,"area":null},{"id":"2309.15223","title":"Low-rank Adaptation of Large Language Model Rescoring for Parameter-Efficient Speech Recognition","created":"2023-09-26","cited_by_count":13,"area":null},{"id":"2312.07887","title":"Learn or Recall? Revisiting Incremental Learning with Pre-trained Language Models","created":"2023-12-13","cited_by_count":13,"area":null},{"id":"2205.14521","title":"Learning Non-Autoregressive Models from Search for Unsupervised Sentence Summarization","created":"2022-05-28","cited_by_count":12,"area":null},{"id":"2206.05658","title":"Improving Pre-trained Language Model Fine-tuning with Noise Stability Regularization","created":"2022-06-12","cited_by_count":12,"area":null},{"id":"2312.01648","title":"Characterizing Large Language Model Geometry Helps Solve Toxicity Detection and Generation","created":"2023-12-04","cited_by_count":12,"area":null},{"id":"2108.12963","title":"Scheduled Sampling Based on Decoding Steps for Neural Machine Translation","created":"2021-08-30","cited_by_count":11,"area":null},{"id":"2212.08853","title":"HyPe: Better Pre-trained Language Model Fine-tuning with Hidden Representation Perturbation","created":"2022-12-17","cited_by_count":11,"area":null},{"id":"2306.12619","title":"Class-Incremental Learning based on Label Generation","created":"2023-06-22","cited_by_count":11,"area":null},{"id":"2106.04072","title":"Coarse-to-Fine Curriculum Learning","created":"2021-06-08","cited_by_count":10,"area":null},{"id":"2205.11603","title":"Representation Projection Invariance Mitigates Representation Collapse","created":"2022-05-23","cited_by_count":10,"area":null},{"id":"2305.04557","title":"Toward Adversarial Training on Contextualized Language Representation","created":"2023-05-08","cited_by_count":10,"area":null},{"id":"2310.17588","title":"PAC-tuning:Fine-tuning Pretrained Language Models with PAC-driven Perturbed Gradient Descent","created":"2023-10-26","cited_by_count":10,"area":null},{"id":"2306.01090","title":"Improving the Robustness of Summarization Systems with Dual Augmentation","created":"2023-06-01","cited_by_count":9,"area":null},{"id":"2206.12608","title":"Adversarial Self-Attention for Language Understanding","created":"2022-06-25","cited_by_count":8,"area":null},{"id":"2209.14569","title":"COLO: A Contrastive Learning based Re-ranking Framework for One-Stage Summarization","created":"2022-09-29","cited_by_count":8,"area":null},{"id":"2212.08390","title":"Lessons learned from the evaluation of Spanish Language Models","created":"2022-12-16","cited_by_count":8,"area":null},{"id":"2202.00901","title":"Retrieve-and-Fill for Scenario-based Task-Oriented Semantic Parsing","created":"2022-02-02","cited_by_count":7,"area":null},{"id":"2205.10227","title":"Label Anchored Contrastive Learning for Language Understanding","created":"2022-04-26","cited_by_count":7,"area":null},{"id":"2301.03252","title":"Active Learning for Abstractive Text Summarization","created":"2023-01-09","cited_by_count":7,"area":null},{"id":"2309.02311","title":"Weigh Your Own Words: Improving Hate Speech Counter Narrative Generation via Attention Regularization","created":"2023-09-05","cited_by_count":7,"area":null},{"id":"2110.06905","title":"Teaching Models new APIs: Domain-Agnostic Simulators for Task Oriented Dialogue","created":"2021-10-13","cited_by_count":6,"area":null},{"id":"2112.08772","title":"Sharpness-Aware Minimization with Dynamic Reweighting","created":"2021-12-16","cited_by_count":6,"area":null},{"id":"2202.09695","title":"SemEval 2022 Task 12: Symlink- Linking Mathematical Symbols to their Descriptions","created":"2022-02-19","cited_by_count":6,"area":null},{"id":"2204.06625","title":"CAMERO: Consistency Regularized Ensemble of Perturbed Language Models with Weight Sharing","created":"2022-04-13","cited_by_count":6,"area":null},{"id":"2204.07834","title":"Bridging Cross-Lingual Gaps During Leveraging the Multilingual Sequence-to-Sequence Pretraining for Text Generation and Understanding","created":"2022-04-16","cited_by_count":6,"area":null},{"id":"2205.00633","title":"Robust Fine-tuning via Perturbation and Interpolation from In-batch Instances","created":"2022-05-02","cited_by_count":6,"area":null},{"id":"2310.03646","title":"TRAM: Bridging Trust Regions and Sharpness Aware Minimization","created":"2023-10-05","cited_by_count":6,"area":null},{"id":"2502.00075","title":"BTS: Harmonizing Specialized Experts into a Generalist LLM","created":"2025-01-31","cited_by_count":6,"area":null},{"id":"2103.09635","title":"SILT: Efficient transformer training for inter-lingual inference","created":"2021-03-17","cited_by_count":5,"area":null},{"id":"2104.05847","title":"Targeted Adversarial Training for Natural Language Understanding","created":"2021-04-12","cited_by_count":5,"area":null},{"id":"2209.07272","title":"Socially Enhanced Situation Awareness from Microblogs using Artificial Intelligence: A Survey","created":"2022-09-13","cited_by_count":5,"area":null},{"id":"2310.16955","title":"Break it, Imitate it, Fix it: Robustness by Generating Human-Like Attacks","created":"2023-10-25","cited_by_count":5,"area":null},{"id":"2411.06710","title":"Model Fusion through Bayesian Optimization in Language Model Fine-Tuning","created":"2024-11-11","cited_by_count":5,"area":null},{"id":"2207.05666","title":"Zero-shot Cross-lingual Transfer is Under-specified Optimization","created":"2022-07-12","cited_by_count":4,"area":null},{"id":"2302.04054","title":"Towards Inferential Reproducibility of Machine Learning Research","created":"2023-02-08","cited_by_count":4,"area":null},{"id":"2402.18905","title":"Characterizing the Training Dynamics of Private Fine-tuning with Langevin diffusion","created":"2024-02-29","cited_by_count":4,"area":null},{"id":"2403.20284","title":"LayerNorm: A key component in parameter-efficient fine-tuning","created":"2024-03-29","cited_by_count":4,"area":null},{"id":"2108.13602","title":"How Does Adversarial Fine-Tuning Benefit BERT?","created":"2021-08-31","cited_by_count":3,"area":null},{"id":"2204.06748","title":"Improving Top-K Decoding for Non-Autoregressive Semantic Parsing via Intent Conditioning","created":"2022-04-14","cited_by_count":3,"area":null},{"id":"2207.06960","title":"Forming Trees with Treeformers","created":"2022-07-14","cited_by_count":3,"area":null},{"id":"2210.12367","title":"Precisely the Point: Adversarial Augmentations for Faithful and Informative Text Generation","created":"2022-10-22","cited_by_count":3,"area":null},{"id":"2312.04032","title":"RoAST: Robustifying Language Models via Adversarial Perturbation with Selective Training","created":"2023-12-07","cited_by_count":3,"area":null},{"id":"2401.10220","title":"AutoFT: Learning an Objective for Robust Fine-Tuning","created":"2024-01-18","cited_by_count":3,"area":null},{"id":"2403.00046","title":"Exploring Data-Efficient Adaptation of Large Language Models for Code Generation","created":"2024-02-29","cited_by_count":3,"area":null},{"id":"2403.06260","title":"SCORE: Self-supervised Correspondence Fine-tuning for Improved Content Representations","created":"2024-03-10","cited_by_count":3,"area":null},{"id":"2505.06889","title":"IM-BERT: Enhancing Robustness of BERT through the Implicit Euler Method","created":"2025-05-11","cited_by_count":3,"area":null},{"id":"2605.08896","title":"FragileFlow: Spectral Control of Correct-but-Fragile Predictions for Foundation Model Robustness","created":"2026-05-09","cited_by_count":3,"area":null},{"id":"2203.10297","title":"Incremental Few-Shot Learning via Implanting and Compressing","created":"2022-03-19","cited_by_count":2,"area":null},{"id":"2210.07916","title":"Style Transfer as Data Augmentation: A Case Study on Named Entity Recognition","created":"2022-10-14","cited_by_count":2,"area":null},{"id":"2210.14979","title":"Robust Domain Adaptation for Pre-trained Multilingual Neural Machine Translation Models","created":"2022-10-26","cited_by_count":2,"area":null},{"id":"2305.17197","title":"Entailment as Robust Self-Learner","created":"2023-05-26","cited_by_count":2,"area":null}],"next_cursor":"100"}