{"total":245,"rows":[{"id":"2111.09543","title":"DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing","created":"2021-11-18","cited_by_count":1254,"area":null},{"id":"2212.03533","title":"Text Embeddings by Weakly-Supervised Contrastive Pre-training","created":"2022-12-07","cited_by_count":1253,"area":null},{"id":"2402.05672","title":"Multilingual E5 Text Embeddings: A Technical Report","created":"2024-02-08","cited_by_count":372,"area":null},{"id":"2207.00112","title":"Language model compression with weighted low-rank factorization","created":"2022-06-30","cited_by_count":195,"area":null},{"id":"2204.06487","title":"Adapting Pre-trained Language Models to African Languages via Multilingual Adaptive Fine-Tuning","created":"2022-04-13","cited_by_count":149,"area":null},{"id":"2407.14679","title":"Compact Language Models via Pruning and Knowledge Distillation","created":"2024-07-19","cited_by_count":148,"area":"distill"},{"id":"2306.08543","title":"MiniLLM: On-Policy Distillation of Large Language Models","created":"2023-06-14","cited_by_count":139,"area":"distill"},{"id":"2504.13203","title":"X-Teaming: Multi-Turn Jailbreaks and Defenses with Adaptive Multi-Agents","created":"2025-04-15","cited_by_count":125,"area":"agents"},{"id":"2103.13076","title":"Finetuning Pretrained Transformers into RNNs","created":"2021-03-24","cited_by_count":96,"area":null},{"id":"2210.01351","title":"Less is More: Task-aware Layer-wise Distillation for Language Model Compression","created":"2022-10-04","cited_by_count":93,"area":null},{"id":"2204.07154","title":"MiniViT: Compressing Vision Transformers with Weight Multiplexing","created":"2022-04-14","cited_by_count":88,"area":null},{"id":"2410.10700","title":"LLMs know their vulnerabilities: Uncover Safety Gaps through Natural Distribution Shifts","created":"2024-10-14","cited_by_count":86,"area":null},{"id":"2407.15831","title":"NV-Retriever: Improving text embedding models with effective hard-negative mining","created":"2024-07-22","cited_by_count":84,"area":"rag"},{"id":"2402.12030","title":"Towards Cross-Tokenizer Distillation: the Universal Logit Distillation Loss for LLMs","created":"2024-02-19","cited_by_count":63,"area":"distill"},{"id":"2106.04570","title":"BERT Learns to Teach: Knowledge Distillation with Meta Learning","created":"2021-06-08","cited_by_count":61,"area":"distill"},{"id":"2405.05374","title":"Arctic-Embed: Scalable, Efficient, and Accurate Text Embedding Models","created":"2024-05-08","cited_by_count":61,"area":null},{"id":"2511.10643","title":"Black-Box On-Policy Distillation of Large Language Models","created":"2025-11-13","cited_by_count":59,"area":"distill"},{"id":"2405.18540","title":"Learning diverse attacks on large language models for robust red-teaming and safety tuning","created":"2024-05-28","cited_by_count":54,"area":"safety"},{"id":"2204.07689","title":"Sparsely Activated Mixture-of-Experts are Robust Multi-Task Learners","created":"2022-04-16","cited_by_count":51,"area":"moe"},{"id":"2203.07228","title":"FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing","created":"2022-03-14","cited_by_count":49,"area":null},{"id":"2112.12731","title":"ERNIE 3.0 Titan: Exploring Larger-scale Knowledge Enhanced Pre-training for Language Understanding and Generation","created":"2021-12-23","cited_by_count":46,"area":null},{"id":"2202.07105","title":"A Survey on Model Compression and Acceleration for Pretrained Language Models","created":"2022-02-15","cited_by_count":45,"area":null},{"id":"2311.07052","title":"Towards the Law of Capacity Gap in Distilling Language Models","created":"2023-11-13","cited_by_count":44,"area":"distill"},{"id":"2304.06845","title":"SemEval-2023 Task 12: Sentiment Analysis for African Languages (AfriSenti-SemEval)","created":"2023-04-13","cited_by_count":41,"area":null},{"id":"2510.06377","title":"Relational Transformer: Toward Zero-Shot Foundation Models for Relational Data","created":"2025-10-07","cited_by_count":39,"area":null},{"id":"2204.07675","title":"MoEBERT: from BERT to Mixture-of-Experts via Importance-Guided Adaptation","created":"2022-04-15","cited_by_count":37,"area":"moe"},{"id":"2303.00807","title":"UDAPDR: Unsupervised Domain Adaptation via LLM Prompting and Distillation of Rerankers","created":"2023-03-01","cited_by_count":37,"area":null},{"id":"2404.14361","title":"Better Synthetic Data by Retrieving and Transforming Existing Datasets","created":"2024-04-22","cited_by_count":36,"area":"synth"},{"id":"2510.24795","title":"A Survey on Efficient Vision-Language-Action Models","created":"2025-10-27","cited_by_count":34,"area":"vla"},{"id":"2205.14443","title":"A Closer Look at Self-Supervised Lightweight Vision Transformers","created":"2022-05-28","cited_by_count":31,"area":null},{"id":"2207.06867","title":"Deep versus Wide: An Analysis of Student Architectures for Task-Agnostic Knowledge Distillation of Self-Supervised Speech Models","created":"2022-07-14","cited_by_count":31,"area":"distill"},{"id":"2410.17215","title":"MiniPLM: Knowledge Distillation for Pre-Training Language Models","created":"2024-10-22","cited_by_count":29,"area":"distill"},{"id":"2305.10010","title":"AD-KD: Attribution-Driven Knowledge Distillation for Language Model Compression","created":"2023-05-17","cited_by_count":26,"area":"distill"},{"id":"2305.13808","title":"Asking Clarification Questions to Handle Ambiguity in Open-Domain QA","created":"2023-05-23","cited_by_count":26,"area":null},{"id":"2406.02396","title":"The Scandinavian Embedding Benchmarks: Comprehensive Assessment of Multilingual and Monolingual Text Embedding","created":"2024-06-04","cited_by_count":26,"area":null},{"id":"2302.09632","title":"HomoDistil: Homotopic Task-Agnostic Distillation of Pre-trained Transformers","created":"2023-02-19","cited_by_count":25,"area":"distill"},{"id":"2304.04280","title":"FrenchMedMCQA: A French Multiple-Choice Question Answering Dataset for Medical domain","created":"2023-04-09","cited_by_count":25,"area":null},{"id":"2305.12129","title":"Lifting the Curse of Capacity Gap in Distilling Language Models","created":"2023-05-20","cited_by_count":25,"area":"moe"},{"id":"2406.17328","title":"Dual-Space Knowledge Distillation for Large Language Models","created":"2024-06-25","cited_by_count":25,"area":"distill"},{"id":"2510.01354","title":"WAInjectBench: Benchmarking Prompt Injection Detections for Web Agents","created":"2025-10-01","cited_by_count":24,"area":"agents"},{"id":"2505.04560","title":"ABKD: Pursuing a Proper Allocation of the Probability Mass in Knowledge Distillation via $\\alpha$-$\\beta$-Divergence","created":"2025-05-07","cited_by_count":23,"area":"distill"},{"id":"2505.23628","title":"AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora","created":"2025-05-29","cited_by_count":23,"area":null},{"id":"2112.08723","title":"Distilled Dual-Encoder Model for Vision-Language Understanding","created":"2021-12-16","cited_by_count":22,"area":null},{"id":"2211.17148","title":"ConvLab-3: A Flexible Dialogue System Toolkit Based on a Unified Data Format","created":"2022-11-30","cited_by_count":22,"area":null},{"id":"2412.20953","title":"GASLITEing the Retrieval: Exploring Vulnerabilities in Dense Embedding-based Search","created":"2024-12-30","cited_by_count":20,"area":"rag"},{"id":"2109.10164","title":"RAIL-KD: RAndom Intermediate Layer Mapping for Knowledge Distillation","created":"2021-09-21","cited_by_count":19,"area":"distill"},{"id":"2206.01859","title":"Extreme Compression for Pre-trained Transformers Made Simple and Efficient","created":"2022-06-04","cited_by_count":19,"area":"distill"},{"id":"2302.14771","title":"Generic-to-Specific Distillation of Masked Autoencoders","created":"2023-02-28","cited_by_count":19,"area":null},{"id":"2306.10014","title":"Coaching a Teachable Student","created":"2023-06-16","cited_by_count":19,"area":"distill"},{"id":"2406.01607","title":"Recent advances in text embedding: A Comprehensive Review of Top-Performing Methods on the MTEB Benchmark","created":"2024-05-27","cited_by_count":19,"area":null},{"id":"2102.04887","title":"NewsBERT: Distilling Pre-trained Language Model for Intelligent News Application","created":"2021-02-09","cited_by_count":17,"area":"distill"},{"id":"2502.11477","title":"Learning to Sample Effective and Diverse Prompts for Text-to-Image Generation","created":"2025-02-17","cited_by_count":17,"area":"diffusion"},{"id":"2305.02031","title":"A Systematic Study of Knowledge Distillation for Natural Language Generation with Pseudo-Target Training","created":"2023-05-03","cited_by_count":16,"area":"distill"},{"id":"2403.10144","title":"NLP Verification: Towards a General Methodology for Certifying Robustness","created":"2024-03-15","cited_by_count":16,"area":null},{"id":"2409.07691","title":"Enhancing Q&A Text Retrieval with Ranking Models: Benchmarking, fine-tuning and deploying Rerankers for RAG","created":"2024-09-12","cited_by_count":16,"area":null},{"id":"2503.16870","title":"Sparse Logit Sampling: Accelerating Knowledge Distillation in LLMs","created":"2025-03-21","cited_by_count":16,"area":"distill"},{"id":"2206.07808","title":"Alexa Teacher Model: Pretraining and Distilling Multi-Billion-Parameter Encoders for Natural Language Understanding Systems","created":"2022-06-15","cited_by_count":15,"area":"distill"},{"id":"2306.06629","title":"GKD: A General Knowledge Distillation Framework for Large-scale Pre-trained Language Model","created":"2023-06-11","cited_by_count":15,"area":"distill"},{"id":"2401.15347","title":"A Comprehensive Survey of Compression Algorithms for Language Models","created":"2024-01-27","cited_by_count":15,"area":null},{"id":"2404.09296","title":"Cross-Data Knowledge Graph Construction for LLM-enabled Educational Question-Answering System: A Case Study at HCMUT","created":"2024-04-14","cited_by_count":14,"area":null},{"id":"2504.18598","title":"BadMoE: Backdooring Mixture-of-Experts LLMs via Optimizing Routing Triggers and Infecting Dormant Experts","created":"2025-04-24","cited_by_count":14,"area":"moe"},{"id":"2511.20030","title":"Cross-Contrastive Clustering for Multimodal Attributed Graphs with Dual Graph Filtering","created":"2025-11-25","cited_by_count":14,"area":null},{"id":"2512.07761","title":"TROJail: Trajectory-Level Optimization for Multi-Turn Large Language Model Jailbreaks with Process Rewards","created":"2025-12-08","cited_by_count":14,"area":"safety"},{"id":"2603.01990","title":"According to Me: Long-Term Personalized Referential Memory QA","created":"2026-03-02","cited_by_count":14,"area":null},{"id":"2211.17135","title":"BudgetLongformer: Can we Cheaply Pretrain a SotA Legal Language Model From Scratch?","created":"2022-11-30","cited_by_count":13,"area":null},{"id":"2304.05659","title":"RIFormer: Keep Your Vision Backbone Effective While Removing Token Mixer","created":"2023-04-12","cited_by_count":13,"area":null},{"id":"2308.05502","title":"Bringing order into the realm of Transformer-based language models for artificial intelligence and law","created":"2023-08-10","cited_by_count":13,"area":null},{"id":"2309.05015","title":"DeViT: Decomposing Vision Transformers for Collaborative Inference in Edge Devices","created":"2023-09-10","cited_by_count":13,"area":null},{"id":"2408.13533","title":"Pandora's Box or Aladdin's Lamp: A Comprehensive Analysis Revealing the Role of RAG Noise in Large Language Models","created":"2024-08-24","cited_by_count":13,"area":"rag"},{"id":"2412.21102","title":"Exploring and Controlling Diversity in LLM-Agent Conversation","created":"2024-12-30","cited_by_count":12,"area":"agents"},{"id":"2602.06854","title":"SEMA: Simple yet Effective Learning for Multi-Turn Jailbreak Attacks","created":"2026-02-06","cited_by_count":12,"area":"safety"},{"id":"2106.00219","title":"Question-aware Transformer Models for Consumer Health Question Summarization","created":"2021-06-01","cited_by_count":11,"area":null},{"id":"2206.14366","title":"Knowledge Distillation of Transformer-based Language Models Revisited","created":"2022-06-29","cited_by_count":11,"area":"distill"},{"id":"2211.11014","title":"Understanding and Improving Knowledge Distillation for Quantization-Aware Training of Large Transformer Encoders","created":"2022-11-20","cited_by_count":11,"area":"distill"},{"id":"2306.14152","title":"Low-Rank Prune-And-Factorize for Language Model Compression","created":"2023-06-25","cited_by_count":11,"area":null},{"id":"2411.06171","title":"SEEKR: Selective Attention-Guided Knowledge Retention for Continual Learning of Large Language Models","created":"2024-11-09","cited_by_count":11,"area":null},{"id":"2512.12069","title":"Rethinking Jailbreak Detection of Large Vision Language Models with Representational Contrastive Scoring","created":"2025-12-12","cited_by_count":11,"area":"lr"},{"id":"2606.06021","title":"OPRD: On-Policy Representation Distillation","created":"2026-06-04","cited_by_count":11,"area":"distill"},{"id":"2303.14334","title":"The Semantic Reader Project: Augmenting Scholarly Documents through AI-Powered Interactive Reading Interfaces","created":"2023-03-25","cited_by_count":9,"area":null},{"id":"2305.15032","title":"How to Distill your BERT: An Empirical Study on the Impact of Weight Initialisation and Distillation Objectives","created":"2023-05-24","cited_by_count":9,"area":"distill"},{"id":"2305.18239","title":"A Study on Knowledge Distillation from Weak Teacher for Scaling Up Pre-trained Language Models","created":"2023-05-26","cited_by_count":9,"area":"distill"},{"id":"2506.12473","title":"TagRouter: Learning Route to LLMs through Tags for Open-Domain Text Generation Tasks","created":"2025-06-14","cited_by_count":9,"area":null},{"id":"2202.08316","title":"FAMIE: A Fast Active Learning Framework for Multilingual Information Extraction","created":"2022-02-16","cited_by_count":8,"area":null},{"id":"2305.09098","title":"Weight-Inherited Distillation for Task-Agnostic BERT Compression","created":"2023-05-16","cited_by_count":8,"area":"distill"},{"id":"2309.11593","title":"Sentence Attention Blocks for Answer Grounding","created":"2023-09-20","cited_by_count":8,"area":null},{"id":"2408.10722","title":"MEGen: Generative Backdoor into Large Language Models via Model Editing","created":"2024-08-20","cited_by_count":8,"area":"safety"},{"id":"2410.09982","title":"Self-Data Distillation for Recovering Quality in Pruned Large Language Models","created":"2024-10-13","cited_by_count":8,"area":null},{"id":"2411.12174","title":"Just KIDDIN: Knowledge Infusion and Distillation for Detection of INdecent Memes","created":"2024-11-19","cited_by_count":8,"area":null},{"id":"2502.05478","title":"OntoTune: Ontology-Driven Self-training for Aligning Large Language Models","created":"2025-02-08","cited_by_count":8,"area":"alignment"},{"id":"2502.20204","title":"Granite Embedding Models","created":"2025-02-27","cited_by_count":8,"area":null},{"id":"2504.01278","title":"Strategize Globally, Adapt Locally: A Multi-Turn Red Teaming Agent with Dual-Level Learning","created":"2025-04-02","cited_by_count":8,"area":"agents"},{"id":"2504.11426","title":"A Dual-Space Framework for General Knowledge Distillation of Large Language Models","created":"2025-04-15","cited_by_count":8,"area":"distill"},{"id":"2205.02340","title":"Knowledge Distillation of Russian Language Models with Reduction of Vocabulary","created":"2022-05-04","cited_by_count":7,"area":"distill"},{"id":"2310.10054","title":"NASH: A Simple Unified Framework of Structured Pruning for Accelerating Encoder-Decoder Language Models","created":"2023-10-16","cited_by_count":7,"area":null},{"id":"2311.00447","title":"On the Opportunities of Green Computing: A Survey","created":"2023-11-01","cited_by_count":7,"area":null},{"id":"2401.01943","title":"Generalist embedding models are better at short-context clinical semantic search than specialized embedding models","created":"2024-01-03","cited_by_count":7,"area":null},{"id":"2402.03223","title":"English Prompts are Better for NLI-based Zero-Shot Emotion Classification than Target-Language Prompts","created":"2024-02-05","cited_by_count":7,"area":null},{"id":"2402.15059","title":"ColBERT-XM: A Modular Multi-Vector Representation Model for Zero-Shot Multilingual Information Retrieval","created":"2024-02-23","cited_by_count":7,"area":null},{"id":"2503.23029","title":"A Retrieval-Augmented Knowledge Mining Method with Deep Thinking LLMs for Biomedical Research and Clinical Support","created":"2025-03-29","cited_by_count":7,"area":"lr"},{"id":"2510.03417","title":"NEXUS: Network Exploration for eXploiting Unsafe Sequences in Multi-Turn LLM Jailbreaks","created":"2025-10-03","cited_by_count":7,"area":"safety"}],"next_cursor":"100"}