{"list":"other","total":96,"rows":[{"id":"2005.14165","title":"Language Models are Few-Shot Learners","created":"2020-05-28","cited_by_count":32419},{"id":"1910.10683","title":"Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer","created":"2019-10-23","cited_by_count":14599},{"id":"1907.11692","title":"RoBERTa: A Robustly Optimized BERT Pretraining Approach","created":"2019-07-26","cited_by_count":13372},{"id":"2005.11401","title":"Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks","created":"2020-05-22","cited_by_count":9297},{"id":"1910.13461","title":"BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension","created":"2019-10-29","cited_by_count":5893},{"id":"1902.00751","title":"Parameter-Efficient Transfer Learning for NLP","created":"2019-02-02","cited_by_count":4236},{"id":"2101.00190","title":"Prefix-Tuning: Optimizing Continuous Prompts for Generation","created":"2021-01-01","cited_by_count":3915},{"id":"2004.04906","title":"Dense Passage Retrieval for Open-Domain Question Answering","created":"2020-04-10","cited_by_count":3824},{"id":"2104.08691","title":"The Power of Scale for Parameter-Efficient Prompt Tuning","created":"2021-04-18","cited_by_count":3795},{"id":"2004.05150","title":"Longformer: The Long-Document Transformer","created":"2020-04-10","cited_by_count":3413},{"id":"1409.3215","title":"Sequence to Sequence Learning with Neural Networks","created":"2014-09-10","cited_by_count":2986},{"id":"1904.09751","title":"The Curious Case of Neural Text Degeneration","created":"2019-04-22","cited_by_count":2916},{"id":"2101.00027","title":"The Pile: An 800GB Dataset of Diverse Text for Language Modeling","created":"2020-12-31","cited_by_count":2411},{"id":"2107.13586","title":"Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing","created":"2021-07-28","cited_by_count":2234},{"id":"1908.02265","title":"ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks","created":"2019-08-06","cited_by_count":2222},{"id":"1909.01066","title":"Language Models as Knowledge Bases?","created":"2019-09-03","cited_by_count":1936},{"id":"1901.02860","title":"Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context","created":"2019-01-09","cited_by_count":1850},{"id":"2001.04451","title":"Reformer: The Efficient Transformer","created":"2020-01-13","cited_by_count":1668},{"id":"2102.09690","title":"Calibrate Before Use: Improving Few-Shot Performance of Language Models","created":"2021-02-19","cited_by_count":1392},{"id":"2012.15723","title":"Making Pre-trained Language Models Better Few-shot Learners","created":"2020-12-31","cited_by_count":1256},{"id":"1805.04833","title":"Hierarchical Neural Story Generation","created":"2018-05-13","cited_by_count":1193},{"id":"2104.08786","title":"Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity","created":"2021-04-18","cited_by_count":1135},{"id":"1911.12543","title":"How Can We Know What Language Models Know?","created":"2019-11-28","cited_by_count":959},{"id":"2001.07676","title":"Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference","created":"2020-01-21","cited_by_count":938},{"id":"1911.00536","title":"DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation","created":"2019-11-01","cited_by_count":842},{"id":"1801.07243","title":"Personalizing Dialogue Agents: I have a dog, do you have pets too?","created":"2018-01-22","cited_by_count":828},{"id":"1810.00278","title":"MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling","created":"2018-09-29","cited_by_count":766},{"id":"2104.07567","title":"Retrieval Augmentation Reduces Hallucination in Conversation","created":"2021-04-15","cited_by_count":692},{"id":"2009.07118","title":"It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners","created":"2020-09-15","cited_by_count":669},{"id":"2106.13884","title":"Multimodal Few-Shot Learning with Frozen Language Models","created":"2021-06-25","cited_by_count":661},{"id":"1710.03957","title":"DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset","created":"2017-10-11","cited_by_count":652},{"id":"2102.07350","title":"Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm","created":"2021-02-15","cited_by_count":606},{"id":"2009.02252","title":"KILT: a Benchmark for Knowledge Intensive Language Tasks","created":"2020-09-04","cited_by_count":573},{"id":"1811.01241","title":"Wizard of Wikipedia: Knowledge-Powered Conversational agents","created":"2018-11-03","cited_by_count":530},{"id":"2001.09977","title":"Towards a Human-like Open-Domain Chatbot","created":"2020-01-27","cited_by_count":470},{"id":"1909.05855","title":"Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Dataset","created":"2019-09-12","cited_by_count":434},{"id":"2010.00904","title":"Autoregressive Entity Retrieval","created":"2020-10-02","cited_by_count":334},{"id":"2005.03754","title":"FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarization","created":"2020-05-07","cited_by_count":293},{"id":"2107.07567","title":"Beyond Goldfish Memory: Long-Term Open-Domain Conversation","created":"2021-07-15","cited_by_count":267},{"id":"2010.15980","title":"AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts","created":"2020-10-29","cited_by_count":249},{"id":"1503.01817","title":"YFCC100M: The New Data in Multimedia Research","created":"2015-03-05","cited_by_count":235},{"id":"2107.07566","title":"Internet-Augmented Dialogue Generation","created":"2021-07-15","cited_by_count":217},{"id":"1905.08743","title":"Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systems","created":"2019-05-21","cited_by_count":216},{"id":"1909.05378","title":"CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases","created":"2019-09-11","cited_by_count":185},{"id":"1908.06083","title":"Build it Break it Fix it for Dialogue Safety: Robustness from Adversarial Human Attack","created":"2019-08-17","cited_by_count":176},{"id":"2109.07830","title":"Reframing Instructional Prompts to GPTk's Language","created":"2021-09-16","cited_by_count":176},{"id":"1911.03842","title":"Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation","created":"2019-11-10","cited_by_count":152},{"id":"1909.05358","title":"Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset","created":"2019-09-01","cited_by_count":140},{"id":"2004.03829","title":"Exploring Versatile Generative Language Model Via Parameter-Efficient Transfer Learning","created":"2020-04-08","cited_by_count":139},{"id":"2106.13353","title":"Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models","created":"2021-06-24","cited_by_count":139},{"id":"1705.05414","title":"Key-Value Retrieval Networks for Task-Oriented Dialogue","created":"2017-05-15","cited_by_count":138},{"id":"2004.08449","title":"Can You Put it All Together: Evaluating Conversational Agents' Ability to Blend Skills","created":"2020-04-17","cited_by_count":131},{"id":"2009.11423","title":"Task-Oriented Dialogue as Dataflow Synthesis","created":"2020-09-24","cited_by_count":122},{"id":"2104.08455","title":"Neural Path Hunter: Reducing Hallucination in Dialogue Systems via Path Grounding","created":"2021-04-17","cited_by_count":106},{"id":"2009.12005","title":"MinTL: Minimalist Transfer Learning for Task-Oriented Dialogue Systems","created":"2020-09-25","cited_by_count":103},{"id":"2109.07684","title":"Language Models are Few-shot Multilingual Learners","created":"2021-09-16","cited_by_count":92},{"id":"1910.10486","title":"Does Gender Matter? Towards Fairness in Dialogue Systems","created":"2019-10-16","cited_by_count":89},{"id":"2012.15504","title":"Continual Learning in Task-Oriented Dialogue Systems","created":"2020-12-31","cited_by_count":83},{"id":"2107.03451","title":"Anticipating Safety Issues in E2E Conversational AI: Framework and Tooling","created":"2021-07-07","cited_by_count":78},{"id":"1811.00945","title":"Image Chat: Engaging Grounded Conversations","created":"2018-11-02","cited_by_count":77},{"id":"1804.08217","title":"Mem2Seq: Effectively Incorporating Knowledge Bases into End-to-End Task-Oriented Dialog Systems","created":"2018-04-23","cited_by_count":70},{"id":"2103.09535","title":"Towards Few-Shot Fact-Checking via Perplexity","created":"2021-03-17","cited_by_count":63},{"id":"2105.04222","title":"Leveraging Slot Descriptions for Zero-Shot Cross-Domain Dialogue State Tracking","created":"2021-05-10","cited_by_count":61},{"id":"2104.08667","title":"SIMMC 2.0: A Task-oriented Dialog Dataset for Immersive Multimodal Conversations","created":"2021-04-18","cited_by_count":56},{"id":"2106.02787","title":"BiToD: A Bilingual Multi-Domain Dataset For Task-Oriented Dialogue Modeling","created":"2021-06-05","cited_by_count":56},{"id":"2109.04655","title":"Zero-Shot Dialogue State Tracking via Cross-Task Transfer","created":"2021-09-10","cited_by_count":52},{"id":"2106.01751","title":"Reordering Examples Helps during Priming-based Few-Shot Learning","created":"2021-06-03","cited_by_count":50},{"id":"2010.12757","title":"Adding Chit-Chat to Enhance Task-Oriented Dialogues","created":"2020-10-24","cited_by_count":48},{"id":"1901.04713","title":"Global-to-local Memory Pointer Networks for Task-Oriented Dialogue","created":"2019-01-15","cited_by_count":47},{"id":"1910.13923","title":"Lightweight and Efficient End-to-End Speech Recognition Using Low-Rank Transformer","created":"2019-10-30","cited_by_count":47},{"id":"1911.03768","title":"The Dialogue Dodecathlon: Open-Domain Knowledge and Image Grounded Conversational Agents","created":"2019-11-09","cited_by_count":45},{"id":"2010.11853","title":"STAR: A Schema-Guided Dialog Dataset for Transfer Learning","created":"2020-10-22","cited_by_count":44},{"id":"2008.06239","title":"Language Models as Few-Shot Learner for Task-Oriented Dialogue Systems","created":"2020-08-14","cited_by_count":41},{"id":"2010.12770","title":"Conversational Semantic Parsing for Dialog State Tracking","created":"2020-10-24","cited_by_count":40},{"id":"2008.12579","title":"The Adapter-Bot: All-In-One Controllable Conversational Model","created":"2020-08-28","cited_by_count":38},{"id":"2009.10855","title":"Controlling Style in Generated Dialogue","created":"2020-09-22","cited_by_count":38},{"id":"2004.11019","title":"Dynamic Fusion Network for Multi-Domain End-to-end Task-Oriented Dialog","created":"2020-04-23","cited_by_count":37},{"id":"2003.07568","title":"XPersona: Evaluating Multilingual Personalized Chatbot","created":"2020-03-17","cited_by_count":36},{"id":"2009.13656","title":"Learning Knowledge Bases with Parameters for Task-Oriented Dialogue Systems","created":"2020-09-28","cited_by_count":35},{"id":"2010.04344","title":"Plug-and-Play Conversational Models","created":"2020-10-09","cited_by_count":30},{"id":"2109.04645","title":"CINS: Comprehensive Instruction for Few-shot Learning in Task-oriented Dialog Systems","created":"2021-09-10","cited_by_count":30},{"id":"2011.03023","title":"Language Model is All You Need: Natural Language Understanding as Question Answering","created":"2020-11-05","cited_by_count":29},{"id":"2109.04137","title":"Fusing task-oriented and open-domain dialogues in conversational agents","created":"2021-09-09","cited_by_count":28},{"id":"1909.06762","title":"Entity-Consistent End-to-end Task-Oriented Dialogue System with KB Retriever","created":"2019-09-15","cited_by_count":21},{"id":"2106.06157","title":"Assessing Political Prudence of Open-domain Chatbots","created":"2021-06-11","cited_by_count":20},{"id":"2109.06513","title":"Exploring Prompt-based Few-shot Learning for Grounded Dialog Generation","created":"2021-09-14","cited_by_count":20},{"id":"1908.04621","title":"Getting To Know You: User Attribute Extraction from Dialogues","created":"2019-08-13","cited_by_count":19},{"id":"2010.01082","title":"Multi-Modal Open-Domain Dialogue","created":"2020-10-02","cited_by_count":19},{"id":"1709.05453","title":"Augmenting End-to-End Dialog Systems with Commonsense Knowledge","created":"2017-09-16","cited_by_count":18},{"id":"1809.05524","title":"Extending Neural Generative Conversational Model using External Knowledge Sources","created":"2018-09-14","cited_by_count":16},{"id":"1810.10647","title":"Multi-level Memory for Task Oriented Dialogs","created":"2018-10-24","cited_by_count":14},{"id":"1908.09982","title":"On the Effectiveness of Low-Rank Matrix Factorization for LSTM Model Compression","created":"2019-08-27","cited_by_count":14},{"id":"2001.01871","title":"Attention over Parameters for Dialogue Systems","created":"2020-01-07","cited_by_count":14},{"id":"2109.12702","title":"Extracting and Inferring Personal Attributes from Dialogue","created":"2021-09-26","cited_by_count":8},{"id":"2109.06762","title":"Greenformer: Factorization Toolkit for Efficient Deep Neural Networks","created":"2021-09-14","cited_by_count":7},{"id":"2109.01958","title":"SideControl: Controlled Open-domain Dialogue Generation via Additive Side Networks","created":"2021-09-05","cited_by_count":5}],"next_cursor":null}