{"total":122,"rows":[{"id":"2111.01243","title":"Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey","created":"2021-11-01","cited_by_count":463,"area":null},{"id":"2102.01017","title":"Measuring and Improving Consistency in Pretrained Language Models","created":"2021-02-01","cited_by_count":364,"area":null},{"id":"2112.10482","title":"ScanQA: 3D Question Answering for Spatial Scene Understanding","created":"2021-12-20","cited_by_count":346,"area":null},{"id":"2209.11755","title":"Promptagator: Few-shot Dense Retrieval From 8 Examples","created":"2022-09-23","cited_by_count":235,"area":null},{"id":"2102.07033","title":"PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them","created":"2021-02-13","cited_by_count":200,"area":null},{"id":"2205.11388","title":"StreamingQA: A Benchmark for Adaptation to New Knowledge over Time in Question Answering Models","created":"2022-05-23","cited_by_count":126,"area":null},{"id":"2104.08678","title":"Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation","created":"2021-04-18","cited_by_count":79,"area":null},{"id":"2205.06938","title":"Generating Literal and Implied Subquestions to Fact-check Complex Claims","created":"2022-05-14","cited_by_count":66,"area":null},{"id":"2205.01883","title":"All You May Need for VQA are Image Captions","created":"2022-05-04","cited_by_count":65,"area":null},{"id":"2101.00438","title":"Few-Shot Question Answering by Pretraining Span Selection","created":"2021-01-02","cited_by_count":62,"area":null},{"id":"2107.13602","title":"Domain-matched Pre-training Tasks for Dense Retrieval","created":"2021-07-28","cited_by_count":53,"area":null},{"id":"2101.00133","title":"NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned","created":"2021-01-01","cited_by_count":52,"area":null},{"id":"2408.01875","title":"Re-Invoke: Tool Invocation Rewriting for Zero-Shot Tool Retrieval","created":"2024-08-03","cited_by_count":52,"area":null},{"id":"2107.11976","title":"One Question Answering Model for Many Languages with Cross-lingual Dense Passage Retrieval","created":"2021-07-26","cited_by_count":46,"area":null},{"id":"2207.00397","title":"Conditional Generation with a Question-Answering Blueprint","created":"2022-07-01","cited_by_count":44,"area":null},{"id":"2105.14682","title":"Zero-shot Fact Verification by Claim Generation","created":"2021-05-31","cited_by_count":42,"area":null},{"id":"2211.11875","title":"Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference","created":"2022-11-21","cited_by_count":40,"area":null},{"id":"2107.07261","title":"Turning Tables: Generating Examples from Semi-structured Tables for Endowing Language Models with Reasoning Skills","created":"2021-07-15","cited_by_count":39,"area":null},{"id":"2404.18443","title":"BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers","created":"2024-04-29","cited_by_count":38,"area":null},{"id":"2104.08303","title":"Capturing Row and Column Semantics in Transformer Based Question Answering over Tables","created":"2021-04-16","cited_by_count":31,"area":null},{"id":"2112.09062","title":"Models in the Loop: Aiding Crowdworkers with Generative Annotation Assistants","created":"2021-12-16","cited_by_count":31,"area":null},{"id":"2205.05019","title":"Learning to Answer Visual Questions from Web Videos","created":"2022-05-10","cited_by_count":30,"area":null},{"id":"2110.07596","title":"Retrieval-guided Counterfactual Generation for QA","created":"2021-10-14","cited_by_count":29,"area":null},{"id":"2305.15387","title":"Peek Across: Improving Multi-Document Modeling via Cross-Document Question-Answering","created":"2023-05-24","cited_by_count":28,"area":null},{"id":"2109.01951","title":"FewshotQA: A simple framework for few-shot learning of question answering tasks using pre-trained text-to-text models","created":"2021-09-04","cited_by_count":26,"area":null},{"id":"2211.08264","title":"QAmeleon: Multilingual QA with Only 5 Examples","created":"2022-11-15","cited_by_count":25,"area":null},{"id":"2203.10741","title":"HIBRIDS: Attention with Hierarchical Biases for Structure-aware Long Document Summarization","created":"2022-03-21","cited_by_count":23,"area":null},{"id":"2405.13003","title":"A Survey on Recent Advances in Conversational Data Generation","created":"2024-05-12","cited_by_count":23,"area":null},{"id":"2109.07954","title":"Improving Unsupervised Question Answering via Summarization-Informed Question Generation","created":"2021-09-16","cited_by_count":21,"area":null},{"id":"2402.17016","title":"Multi-Task Contrastive Learning for 8192-Token Bilingual Text Embeddings","created":"2024-02-26","cited_by_count":21,"area":null},{"id":"2108.13854","title":"Contrastive Domain Adaptation for Question Answering using Limited Text Corpora","created":"2021-08-31","cited_by_count":20,"area":null},{"id":"2201.07745","title":"Improving Biomedical Information Retrieval with Neural Retrievers","created":"2022-01-19","cited_by_count":20,"area":null},{"id":"2106.08190","title":"Question Answering Infused Pre-training of General-Purpose Contextualized Representations","created":"2021-06-15","cited_by_count":19,"area":null},{"id":"2112.10728","title":"MuMuQA: Multimedia Multi-Hop News Question Answering via Cross-Media Knowledge Extraction and Grounding","created":"2021-12-20","cited_by_count":19,"area":null},{"id":"2302.01691","title":"LIQUID: A Framework for List Question Answering Dataset Generation","created":"2023-02-03","cited_by_count":19,"area":null},{"id":"2402.13492","title":"Retrieval Helps or Hurts? A Deeper Dive into the Efficacy of Retrieval Augmentation to Language Models","created":"2024-02-21","cited_by_count":19,"area":null},{"id":"2205.12609","title":"Generating Information-Seeking Conversations from Unlabeled Documents","created":"2022-05-25","cited_by_count":17,"area":null},{"id":"2209.11000","title":"Selecting Better Samples from Pre-trained LLMs: A Case Study on Question Generation","created":"2022-09-22","cited_by_count":17,"area":null},{"id":"2212.10449","title":"Socratic Pretraining: Question-Driven Pretraining for Controllable Summarization","created":"2022-12-20","cited_by_count":17,"area":null},{"id":"2309.07822","title":"CATfOOD: Counterfactual Augmented Training for Improving Out-of-Domain Performance and Calibration","created":"2023-09-14","cited_by_count":17,"area":null},{"id":"2108.12560","title":"QACE: Asking Questions to Evaluate an Image Caption","created":"2021-08-28","cited_by_count":15,"area":null},{"id":"2305.17002","title":"An Empirical Comparison of LM-based Question and Answer Generation Methods","created":"2023-05-26","cited_by_count":15,"area":null},{"id":"2503.01151","title":"ReaderLM-v2: Small Language Model for HTML to Markdown and JSON","created":"2025-03-03","cited_by_count":15,"area":null},{"id":"2104.01099","title":"TAPAS at SemEval-2021 Task 9: Reasoning over tables with intermediate pre-training","created":"2021-04-02","cited_by_count":14,"area":null},{"id":"2208.03197","title":"Low-Resource Dense Retrieval for Open-Domain Question Answering: A Comprehensive Survey","created":"2022-08-05","cited_by_count":14,"area":null},{"id":"2301.02998","title":"InPars-Light: Cost-Effective Unsupervised Training of Efficient Rankers","created":"2023-01-08","cited_by_count":14,"area":null},{"id":"2508.09494","title":"Learning Facts at Scale with Active Reading","created":"2025-08-13","cited_by_count":13,"area":null},{"id":"2203.08926","title":"Synthetic Question Value Estimation for Domain Adaptation of Question Answering","created":"2022-03-16","cited_by_count":12,"area":null},{"id":"2310.05007","title":"MinPrompt: Graph-based Minimal Prompt Data Augmentation for Few-shot Question Answering","created":"2023-10-08","cited_by_count":12,"area":null},{"id":"2211.10330","title":"GENIUS: Sketch-based Language Model Pre-training via Extreme and Selective Masking for Text Generation and Augmentation","created":"2022-11-18","cited_by_count":11,"area":null},{"id":"2112.08342","title":"DG2: Data Augmentation Through Document Grounded Dialogue Generation","created":"2021-12-15","cited_by_count":10,"area":null},{"id":"2304.12206","title":"PAXQA: Generating Cross-lingual Question Answering Examples at Training Scale","created":"2023-04-24","cited_by_count":10,"area":null},{"id":"2311.07930","title":"It's All Relative! -- A Synthetic Query Generation Approach for Improving Zero-Shot Relevance Prediction","created":"2023-11-14","cited_by_count":10,"area":null},{"id":"2209.05401","title":"MaXM: Towards Multilingual Visual Question Answering","created":"2022-09-12","cited_by_count":9,"area":null},{"id":"2305.11944","title":"Exploring the Viability of Synthetic Query Generation for Relevance Prediction","created":"2023-05-19","cited_by_count":9,"area":null},{"id":"2309.12426","title":"Can LLMs Augment Low-Resource Reading Comprehension Datasets? Opportunities and Challenges","created":"2023-09-21","cited_by_count":9,"area":null},{"id":"2104.08801","title":"Back-Training excels Self-Training at Unsupervised Domain Adaptation of Question Generation and Passage Retrieval","created":"2021-04-18","cited_by_count":8,"area":null},{"id":"2305.07984","title":"SCENE: Self-Labeled Counterfactuals for Extrapolating to Negative Examples","created":"2023-05-13","cited_by_count":8,"area":null},{"id":"2310.07830","title":"Does Synthetic Data Make Large Language Models More Efficient?","created":"2023-10-11","cited_by_count":8,"area":null},{"id":"2102.12128","title":"OneStop QAMaker: Extract Question-Answer Pairs from Text in a One-Stop Approach","created":"2021-02-24","cited_by_count":7,"area":null},{"id":"2205.08056","title":"\"What makes a question inquisitive?\" A Study on Type-Controlled Inquisitive Question Generation","created":"2022-05-17","cited_by_count":7,"area":null},{"id":"2404.00571","title":"Explainable Multi-hop Question Generation: An End-to-End Approach without Intermediate Question Labeling","created":"2024-03-31","cited_by_count":7,"area":null},{"id":"2404.12145","title":"From Form(s) to Meaning: Probing the Semantic Depths of Language Models Using Multisense Consistency","created":"2024-04-18","cited_by_count":7,"area":null},{"id":"2502.11181","title":"Improving Scientific Document Retrieval with Concept Coverage-based Query Set Generation","created":"2025-02-16","cited_by_count":7,"area":null},{"id":"2506.11117","title":"ScIRGen: Synthesize Realistic and Large-Scale RAG Dataset for Scientific Research","created":"2025-06-09","cited_by_count":7,"area":null},{"id":"2102.01226","title":"Self-Teaching Machines to Read and Comprehend with Large-Scale Multi-Subject Question-Answering Data","created":"2021-02-01","cited_by_count":6,"area":null},{"id":"2203.08685","title":"A Feasibility Study of Answer-Agnostic Question Generation for Education","created":"2022-03-16","cited_by_count":6,"area":null},{"id":"2204.09248","title":"Synthetic Target Domain Supervision for Open Retrieval QA","created":"2022-04-20","cited_by_count":6,"area":null},{"id":"2210.06781","title":"Closed-book Question Generation via Contrastive Learning","created":"2022-10-13","cited_by_count":6,"area":null},{"id":"2210.11536","title":"CONSISTENT: Open-Ended Question Generation From News Articles","created":"2022-10-20","cited_by_count":6,"area":null},{"id":"2212.10528","title":"HYRR: Hybrid Infused Reranking for Passage Retrieval","created":"2022-12-20","cited_by_count":6,"area":null},{"id":"2305.03088","title":"Modeling What-to-ask and How-to-ask for Answer-unaware Conversational Question Generation","created":"2023-05-04","cited_by_count":6,"area":null},{"id":"2309.06358","title":"Generative Data Augmentation using LLMs improves Distributional Robustness in Question Answering","created":"2023-09-03","cited_by_count":6,"area":null},{"id":"2204.04711","title":"Data Augmentation for Biomedical Factoid Question Answering","created":"2022-04-10","cited_by_count":5,"area":null},{"id":"2204.11373","title":"Entity-Conditioned Question Generation for Robust Attention Distribution in Neural Information Retrieval","created":"2022-04-24","cited_by_count":5,"area":null},{"id":"2211.14880","title":"Combining Data Generation and Active Learning for Low-Resource Question Answering","created":"2022-11-27","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":"2310.05295","title":"Visual Storytelling with Question-Answer Plans","created":"2023-10-08","cited_by_count":5,"area":null},{"id":"2405.06687","title":"Hire Me or Not? Examining Language Model's Behavior with Occupation Attributes","created":"2024-05-06","cited_by_count":5,"area":null},{"id":"2210.13778","title":"IDK-MRC: Unanswerable Questions for Indonesian Machine Reading Comprehension","created":"2022-10-25","cited_by_count":4,"area":null},{"id":"2301.09715","title":"PrimeQA: The Prime Repository for State-of-the-Art Multilingual Question Answering Research and Development","created":"2023-01-23","cited_by_count":4,"area":null},{"id":"2311.15564","title":"Boot and Switch: Alternating Distillation for Zero-Shot Dense Retrieval","created":"2023-11-27","cited_by_count":4,"area":null},{"id":"2605.15156","title":"MeMo: Memory as a Model","created":"2026-05-14","cited_by_count":4,"area":null},{"id":"2211.12641","title":"Leveraging Data Recasting to Enhance Tabular Reasoning","created":"2022-11-23","cited_by_count":3,"area":null},{"id":"2309.04739","title":"Data Augmentation for Conversational AI","created":"2023-09-09","cited_by_count":3,"area":null},{"id":"2311.15563","title":"Noisy Self-Training with Synthetic Queries for Dense Retrieval","created":"2023-11-27","cited_by_count":3,"area":null},{"id":"2401.13594","title":"Graph Guided Question Answer Generation for Procedural Question-Answering","created":"2024-01-24","cited_by_count":3,"area":null},{"id":"2404.09163","title":"GeMQuAD : Generating Multilingual Question Answering Datasets from Large Language Models using Few Shot Learning","created":"2024-04-14","cited_by_count":3,"area":null},{"id":"2410.10372","title":"BookWorm: A Dataset for Character Description and Analysis","created":"2024-10-14","cited_by_count":3,"area":null},{"id":"2505.15856","title":"DisastIR: A Comprehensive Information Retrieval Benchmark for Disaster Management","created":"2025-05-20","cited_by_count":3,"area":null},{"id":"2505.21999","title":"Found in Translation: Measuring Multilingual LLM Consistency as Simple as Translate then Evaluate","created":"2025-05-28","cited_by_count":3,"area":null},{"id":"2104.08744","title":"Generative Context Pair Selection for Multi-hop Question Answering","created":"2021-04-18","cited_by_count":2,"area":null},{"id":"2107.05002","title":"Improving Low-resource Reading Comprehension via Cross-lingual Transposition Rethinking","created":"2021-07-11","cited_by_count":2,"area":null},{"id":"2109.04689","title":"Generating Self-Contained and Summary-Centric Question Answer Pairs via Differentiable Reward Imitation Learning","created":"2021-09-10","cited_by_count":2,"area":null},{"id":"2203.09073","title":"Ask to Understand: Question Generation for Multi-hop Question Answering","created":"2022-03-17","cited_by_count":2,"area":null},{"id":"2204.08009","title":"WikiOmnia: generative QA corpus on the whole Russian Wikipedia","created":"2022-04-17","cited_by_count":2,"area":null},{"id":"2208.01299","title":"To Answer or Not to Answer? Improving Machine Reading Comprehension Model with Span-based Contrastive Learning","created":"2022-08-02","cited_by_count":2,"area":null},{"id":"2211.08850","title":"Consecutive Question Generation via Dynamic Multitask Learning","created":"2022-11-16","cited_by_count":2,"area":null},{"id":"2211.16971","title":"A Pipeline for Generating, Annotating and Employing Synthetic Data for Real World Question Answering","created":"2022-11-30","cited_by_count":2,"area":null},{"id":"2311.10614","title":"A Self-enhancement Approach for Domain-specific Chatbot Training via Knowledge Mining and Digest","created":"2023-11-17","cited_by_count":2,"area":null}],"next_cursor":"100"}