{"total":60,"rows":[{"id":"2209.09513","title":"Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering","created":"2022-09-20","cited_by_count":1919,"area":"lr"},{"id":"2108.00573","title":"MuSiQue: Multihop Questions via Single-hop Question Composition","created":"2021-08-02","cited_by_count":916,"area":null},{"id":"2203.07540","title":"ScienceWorld: Is your Agent Smarter than a 5th Grader?","created":"2022-03-14","cited_by_count":327,"area":"lr"},{"id":"2104.08661","title":"Explaining Answers with Entailment Trees","created":"2021-04-17","cited_by_count":152,"area":null},{"id":"2102.12060","title":"Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processing","created":"2021-02-24","cited_by_count":126,"area":null},{"id":"2503.05179","title":"Sketch-of-Thought: Efficient LLM Reasoning with Adaptive Cognitive-Inspired Sketching","created":"2025-03-07","cited_by_count":117,"area":"lr"},{"id":"2503.23077","title":"Efficient Inference for Large Reasoning Models: A Survey","created":"2025-03-29","cited_by_count":61,"area":"lr"},{"id":"2305.03453","title":"T-SciQ: Teaching Multimodal Chain-of-Thought Reasoning via Mixed Large Language Model Signals for Science Question Answering","created":"2023-05-05","cited_by_count":60,"area":"lr"},{"id":"2104.07644","title":"ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoning","created":"2021-04-15","cited_by_count":58,"area":null},{"id":"2504.00125","title":"LLMs for Explainable AI: A Comprehensive Survey","created":"2025-03-31","cited_by_count":45,"area":null},{"id":"2109.02593","title":"General-Purpose Question-Answering with Macaw","created":"2021-09-06","cited_by_count":40,"area":null},{"id":"2405.01379","title":"Verification and Refinement of Natural Language Explanations through LLM-Symbolic Theorem Proving","created":"2024-05-02","cited_by_count":35,"area":null},{"id":"2103.08545","title":"A Study of Automatic Metrics for the Evaluation of Natural Language Explanations","created":"2021-03-15","cited_by_count":34,"area":null},{"id":"2106.11988","title":"On the Diversity and Limits of Human Explanations","created":"2021-06-22","cited_by_count":30,"area":null},{"id":"2101.10421","title":"English Machine Reading Comprehension Datasets: A Survey","created":"2021-01-25","cited_by_count":26,"area":null},{"id":"2402.01512","title":"Distractor Generation in Multiple-Choice Tasks: A Survey of Methods, Datasets, and Evaluation","created":"2024-02-02","cited_by_count":25,"area":null},{"id":"2312.04021","title":"A Study on the Calibration of In-context Learning","created":"2023-12-07","cited_by_count":22,"area":null},{"id":"2401.04531","title":"MERA: A Comprehensive LLM Evaluation in Russian","created":"2024-01-09","cited_by_count":22,"area":null},{"id":"2210.13513","title":"ExPUNations: Augmenting Puns with Keywords and Explanations","created":"2022-10-24","cited_by_count":21,"area":null},{"id":"2107.11879","title":"Hybrid Autoregressive Inference for Scalable Multi-hop Explanation Regeneration","created":"2021-07-25","cited_by_count":17,"area":null},{"id":"2106.01354","title":"multiPRover: Generating Multiple Proofs for Improved Interpretability in Rule Reasoning","created":"2021-06-02","cited_by_count":15,"area":null},{"id":"2302.06729","title":"STREET: A Multi-Task Structured Reasoning and Explanation Benchmark","created":"2023-02-13","cited_by_count":13,"area":"lr"},{"id":"2105.01974","title":"Do Natural Language Explanations Represent Valid Logical Arguments? Verifying Entailment in Explainable NLI Gold Standards","created":"2021-05-05","cited_by_count":12,"area":null},{"id":"2210.12813","title":"TAPE: Assessing Few-shot Russian Language Understanding","created":"2022-10-23","cited_by_count":11,"area":null},{"id":"2109.06853","title":"Summarize-then-Answer: Generating Concise Explanations for Multi-hop Reading Comprehension","created":"2021-09-14","cited_by_count":10,"area":null},{"id":"2407.03545","title":"On Evaluating Explanation Utility for Human-AI Decision Making in NLP","created":"2024-07-03","cited_by_count":9,"area":null},{"id":"2505.24264","title":"Faithful and Robust LLM-Driven Theorem Proving for NLI Explanations","created":"2025-05-30","cited_by_count":8,"area":null},{"id":"2105.03417","title":"Diff-Explainer: Differentiable Convex Optimization for Explainable Multi-hop Inference","created":"2021-05-07","cited_by_count":7,"area":null},{"id":"2211.07517","title":"Are Hard Examples also Harder to Explain? A Study with Human and Model-Generated Explanations","created":"2022-11-14","cited_by_count":7,"area":null},{"id":"2212.01692","title":"Can In-context Learners Learn a Reasoning Concept from Demonstrations?","created":"2022-12-03","cited_by_count":7,"area":"lr"},{"id":"2305.02993","title":"SemEval-2023 Task 7: Multi-Evidence Natural Language Inference for Clinical Trial Data","created":"2023-05-04","cited_by_count":7,"area":null},{"id":"2305.01713","title":"Learning Disentangled Semantic Spaces of Explanations via Invertible Neural Networks","created":"2023-05-02","cited_by_count":6,"area":null},{"id":"2311.08579","title":"Graph-Induced Syntactic-Semantic Spaces in Transformer-Based Variational AutoEncoders","created":"2023-11-14","cited_by_count":6,"area":null},{"id":"2402.00723","title":"Improving Semantic Control in Discrete Latent Spaces with Transformer Quantized Variational Autoencoders","created":"2024-02-01","cited_by_count":6,"area":null},{"id":"2109.03334","title":"On the Challenges of Evaluating Compositional Explanations in Multi-Hop Inference: Relevance, Completeness, and Expert Ratings","created":"2021-09-07","cited_by_count":5,"area":null},{"id":"2210.06230","title":"Quasi-symbolic Semantic Geometry over Transformer-based Variational AutoEncoder","created":"2022-10-12","cited_by_count":5,"area":null},{"id":"2305.11789","title":"Solving NLP Problems through Human-System Collaboration: A Discussion-based Approach","created":"2023-05-19","cited_by_count":5,"area":null},{"id":"2605.25421","title":"HyLaT: Efficient Multi-Agent Communication via Hybrid Latent-Text Protocol","created":"2026-05-25","cited_by_count":4,"area":"agents"},{"id":"2105.05737","title":"Encoding Explanatory Knowledge for Zero-shot Science Question Answering","created":"2021-05-12","cited_by_count":3,"area":null},{"id":"2312.13208","title":"LlaMaVAE: Guiding Large Language Model Generation via Continuous Latent Sentence Spaces","created":"2023-12-20","cited_by_count":3,"area":null},{"id":"2507.10085","title":"Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning","created":"2025-07-14","cited_by_count":3,"area":"lr"},{"id":"2211.05452","title":"Towards Human-Centred Explainability Benchmarks For Text Classification","created":"2022-11-10","cited_by_count":2,"area":null},{"id":"2403.09703","title":"Concept-aware Data Construction Improves In-context Learning of Language Models","created":"2024-03-08","cited_by_count":2,"area":null},{"id":"2506.19418","title":"Learning to Disentangle Latent Reasoning Rules with Language VAEs: A Systematic Study","created":"2025-06-24","cited_by_count":2,"area":"lr"},{"id":"2508.07023","title":"MV-CoRe: Multimodal Visual-Conceptual Reasoning for Complex Visual Question Answering","created":"2025-08-09","cited_by_count":2,"area":"lr"},{"id":"2205.01809","title":"Scientific Explanation and Natural Language: A Unified Epistemological-Linguistic Perspective for Explainable AI","created":"2022-05-03","cited_by_count":1,"area":null},{"id":"2208.03339","title":"Going Beyond Approximation: Encoding Constraints for Explainable Multi-hop Inference via Differentiable Combinatorial Solvers","created":"2022-08-05","cited_by_count":1,"area":null},{"id":"2306.00652","title":"Explanation Graph Generation via Generative Pre-training over Synthetic Graphs","created":"2023-06-01","cited_by_count":1,"area":"lr"},{"id":"2308.03581","title":"Towards Controllable Natural Language Inference through Lexical Inference Types","created":"2023-08-07","cited_by_count":1,"area":null},{"id":"2309.07624","title":"Dynamic MOdularized Reasoning for Compositional Structured Explanation Generation","created":"2023-09-14","cited_by_count":1,"area":null},{"id":"2404.02625","title":"A Differentiable Integer Linear Programming Solver for Explanation-Based Natural Language Inference","created":"2024-04-03","cited_by_count":1,"area":null},{"id":"2406.02804","title":"ACCORD: Closing the Commonsense Measurability Gap","created":"2024-06-04","cited_by_count":1,"area":"lr"},{"id":"2504.03640","title":"Bonsai: Interpretable Tree-Adaptive Grounded Reasoning","created":"2025-04-04","cited_by_count":1,"area":null},{"id":"2101.06573","title":"Understanding in Artificial Intelligence","created":"2021-01-17","cited_by_count":0,"area":null},{"id":"2110.09036","title":"Ranking Facts for Explaining Answers to Elementary Science Questions","created":"2021-10-18","cited_by_count":0,"area":null},{"id":"2208.01376","title":"Active entailment encoding for explanation tree construction using parsimonious generation of hard negatives","created":"2022-08-02","cited_by_count":0,"area":"lr"},{"id":"2309.08347","title":"Reward Engineering for Generating Semi-structured Explanation","created":"2023-09-15","cited_by_count":0,"area":null},{"id":"2506.20083","title":"Bridging Compositional and Distributional Semantics: A Survey on Latent Semantic Geometry via AutoEncoder","created":"2025-06-25","cited_by_count":0,"area":"mechinterp"},{"id":"2601.08742","title":"Inferring Latent Intentions: Attributional Natural Language Inference in LLM Agents","created":"2026-01-13","cited_by_count":0,"area":"agents"},{"id":"2605.15436","title":"Neural Activation Patterns Across Language Model Architectures: A Comprehensive Analysis of Cognitive Task Performance","created":"2026-05-14","cited_by_count":0,"area":null}],"next_cursor":null}