{"total":18,"rows":[{"id":"2201.11473","title":"Reasoning Like Program Executors","created":"2022-01-27","cited_by_count":45,"area":"lr"},{"id":"2301.10896","title":"Causal Reasoning of Entities and Events in Procedural Texts","created":"2023-01-26","cited_by_count":23,"area":"lr"},{"id":"2201.05880","title":"Reasoning over Hybrid Chain for Table-and-Text Open Domain QA","created":"2022-01-15","cited_by_count":10,"area":"lr"},{"id":"2210.07138","title":"Counterfactual Multihop QA: A Cause-Effect Approach for Reducing Disconnected Reasoning","created":"2022-10-13","cited_by_count":7,"area":"lr"},{"id":"2208.10297","title":"Locate Then Ask: Interpretable Stepwise Reasoning for Multi-hop Question Answering","created":"2022-08-22","cited_by_count":6,"area":"lr"},{"id":"2407.02964","title":"FSM: A Finite State Machine Based Zero-Shot Prompting Paradigm for Multi-Hop Question Answering","created":"2024-07-03","cited_by_count":6,"area":"lr"},{"id":"2305.14237","title":"HOP, UNION, GENERATE: Explainable Multi-hop Reasoning without Rationale Supervision","created":"2023-05-23","cited_by_count":5,"area":"lr"},{"id":"2310.12557","title":"DepWiGNN: A Depth-wise Graph Neural Network for Multi-hop Spatial Reasoning in Text","created":"2023-10-19","cited_by_count":3,"area":"lr"},{"id":"2510.02827","title":"StepChain GraphRAG: Reasoning Over Knowledge Graphs for Multi-Hop Question Answering","created":"2025-10-03","cited_by_count":3,"area":"lr"},{"id":"2207.01450","title":"Discourse-Aware Graph Networks for Textual Logical Reasoning","created":"2022-07-04","cited_by_count":2,"area":"lr"},{"id":"2212.00482","title":"IRRGN: An Implicit Relational Reasoning Graph Network for Multi-turn Response Selection","created":"2022-12-01","cited_by_count":2,"area":"lr"},{"id":"2302.05963","title":"Analyzing the Effectiveness of the Underlying Reasoning Tasks in Multi-hop Question Answering","created":"2023-02-12","cited_by_count":2,"area":"lr"},{"id":"2410.17021","title":"SG-FSM: A Self-Guiding Zero-Shot Prompting Paradigm for Multi-Hop Question Answering Based on Finite State Machine","created":"2024-10-22","cited_by_count":2,"area":"lr"},{"id":"2210.05208","title":"How Well Do Multi-hop Reading Comprehension Models Understand Date Information?","created":"2022-10-11","cited_by_count":1,"area":"lr"},{"id":"2210.06418","title":"Relational Graph Convolutional Neural Networks for Multihop Reasoning: A Comparative Study","created":"2022-10-12","cited_by_count":1,"area":"lr"},{"id":"2305.03356","title":"From Parse-Execute to Parse-Execute-Refine: Improving Semantic Parser for Complex Question Answering over Knowledge Base","created":"2023-05-05","cited_by_count":1,"area":"lr"},{"id":"2306.12069","title":"Modeling Hierarchical Reasoning Chains by Linking Discourse Units and Key Phrases for Reading Comprehension","created":"2023-06-21","cited_by_count":1,"area":"lr"},{"id":"2601.21699","title":"Can David Beat Goliath? On Multi-Hop Reasoning with Resource-Constrained Agents","created":"2026-01-29","cited_by_count":1,"area":"lr"}],"next_cursor":null}