{"total":41,"rows":[{"id":"2106.07139","title":"Pre-Trained Models: Past, Present and Future","created":"2021-06-14","cited_by_count":319,"area":null},{"id":"2305.05644","title":"Towards Building the Federated GPT: Federated Instruction Tuning","created":"2023-05-09","cited_by_count":185,"area":null},{"id":"2101.00774","title":"Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering","created":"2021-01-04","cited_by_count":178,"area":null},{"id":"2104.07302","title":"TransferNet: An Effective and Transparent Framework for Multi-hop Question Answering over Relation Graph","created":"2021-04-15","cited_by_count":60,"area":null},{"id":"2306.03805","title":"The Emergence of Essential Sparsity in Large Pre-trained Models: The Weights that Matter","created":"2023-06-06","cited_by_count":42,"area":null},{"id":"2110.13472","title":"Decomposing Complex Questions Makes Multi-Hop QA Easier and More Interpretable","created":"2021-10-26","cited_by_count":35,"area":null},{"id":"2109.06747","title":"Adaptive Information Seeking for Open-Domain Question Answering","created":"2021-09-14","cited_by_count":29,"area":null},{"id":"2306.10460","title":"Instant Soup: Cheap Pruning Ensembles in A Single Pass Can Draw Lottery Tickets from Large Models","created":"2023-06-18","cited_by_count":20,"area":null},{"id":"2305.03130","title":"Chain-of-Skills: A Configurable Model for Open-domain Question Answering","created":"2023-05-04","cited_by_count":19,"area":null},{"id":"2102.10301","title":"Towards Accurate and Compact Architectures via Neural Architecture Transformer","created":"2021-02-20","cited_by_count":17,"area":null},{"id":"2311.11482","title":"Meta Prompting for AI Systems","created":"2023-11-20","cited_by_count":16,"area":null},{"id":"2103.12876","title":"Complex Factoid Question Answering with a Free-Text Knowledge Graph","created":"2021-03-23","cited_by_count":14,"area":null},{"id":"2107.11823","title":"Graph-free Multi-hop Reading Comprehension: A Select-to-Guide Strategy","created":"2021-07-25","cited_by_count":13,"area":null},{"id":"2103.10574","title":"Hopper: Multi-hop Transformer for Spatiotemporal Reasoning","created":"2021-03-19","cited_by_count":12,"area":null},{"id":"2205.11729","title":"From Easy to Hard: Two-stage Selector and Reader for Multi-hop Question Answering","created":"2022-05-24","cited_by_count":12,"area":null},{"id":"2103.00820","title":"Learning Reasoning Paths over Semantic Graphs for Video-grounded Dialogues","created":"2021-03-01","cited_by_count":11,"area":null},{"id":"2402.13551","title":"Fine-Grained Modeling of Narrative Context: A Coherence Perspective via Retrospective Questions","created":"2024-02-21","cited_by_count":11,"area":null},{"id":"2307.04661","title":"On the power of graph neural networks and the role of the activation function","created":"2023-07-10","cited_by_count":8,"area":null},{"id":"2407.08642","title":"Towards Building Specialized Generalist AI with System 1 and System 2 Fusion","created":"2024-07-11","cited_by_count":8,"area":null},{"id":"2409.17510","title":"NeuroPath: A Neural Pathway Transformer for Joining the Dots of Human Connectomes","created":"2024-09-26","cited_by_count":6,"area":null},{"id":"2102.03741","title":"Memory Augmented Sequential Paragraph Retrieval for Multi-hop Question Answering","created":"2021-02-07","cited_by_count":4,"area":null},{"id":"2112.09658","title":"Reasoning Chain Based Adversarial Attack for Multi-hop Question Answering","created":"2021-12-17","cited_by_count":4,"area":null},{"id":"2308.06431","title":"Performance Prediction for Multi-hop Questions","created":"2023-08-12","cited_by_count":4,"area":null},{"id":"2105.05737","title":"Encoding Explanatory Knowledge for Zero-shot Science Question Answering","created":"2021-05-12","cited_by_count":3,"area":null},{"id":"2106.09983","title":"Weakly Supervised Pre-Training for Multi-Hop Retriever","created":"2021-06-18","cited_by_count":3,"area":null},{"id":"2407.14996","title":"All Against Some: Efficient Integration of Large Language Models for Message Passing in Graph Neural Networks","created":"2024-07-20","cited_by_count":3,"area":null},{"id":"2107.00368","title":"Ensemble Learning-Based Approach for Improving Generalization Capability of Machine Reading Comprehension Systems","created":"2021-07-01","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":"2203.09073","title":"Ask to Understand: Question Generation for Multi-hop Question Answering","created":"2022-03-17","cited_by_count":2,"area":null},{"id":"2104.00218","title":"Integrating Subgraph-aware Relation and DirectionReasoning for Question Answering","created":"2021-04-01","cited_by_count":1,"area":null},{"id":"2107.00841","title":"ClueReader: Heterogeneous Graph Attention Network for Multi-hop Machine Reading Comprehension","created":"2021-07-02","cited_by_count":1,"area":null},{"id":"2207.05561","title":"Brain-inspired Graph Spiking Neural Networks for Commonsense Knowledge Representation and Reasoning","created":"2022-07-11","cited_by_count":1,"area":null},{"id":"2212.04072","title":"A Comprehensive Survey on Multi-hop Machine Reading Comprehension Approaches","created":"2022-12-08","cited_by_count":1,"area":null},{"id":"2112.06189","title":"An original model for multi-target learning of logical rules for knowledge graph reasoning","created":"2021-12-12","cited_by_count":0,"area":null},{"id":"2206.07556","title":"KE-QI: A Knowledge Enhanced Article Quality Identification Dataset","created":"2022-06-15","cited_by_count":0,"area":null},{"id":"2210.08242","title":"RAPS: A Novel Few-Shot Relation Extraction Pipeline with Query-Information Guided Attention and Adaptive Prototype Fusion","created":"2022-10-15","cited_by_count":0,"area":null},{"id":"2311.02345","title":"Perturbation-based Active Learning for Question Answering","created":"2023-11-04","cited_by_count":0,"area":null},{"id":"2402.08085","title":"Message Detouring: A Simple Yet Effective Cycle Representation for Expressive Graph Learning","created":"2024-02-12","cited_by_count":0,"area":null},{"id":"2407.18479","title":"Multi-turn Response Selection with Commonsense-enhanced Language Models","created":"2024-07-26","cited_by_count":0,"area":null},{"id":"2512.12688","title":"How Prompts Move Language Model Behavior: Frames, Salience, and Construal as Semantic Control","created":"2025-12-14","cited_by_count":0,"area":null},{"id":"2609.35193","title":"ConRAG: Lightweight inference of multi-hop relations","created":"2026-09-28","cited_by_count":0,"area":null}],"next_cursor":null}