{"total":48,"rows":[{"id":"2210.13966","title":"The Debate Over Understanding in AI's Large Language Models","created":"2022-10-14","cited_by_count":142,"area":null},{"id":"2103.00242","title":"A Survey on Stance Detection for Mis- and Disinformation Identification","created":"2021-02-27","cited_by_count":75,"area":null},{"id":"2106.00840","title":"Comparing Test Sets with Item Response Theory","created":"2021-06-01","cited_by_count":57,"area":null},{"id":"2105.03659","title":"Logic-Driven Context Extension and Data Augmentation for Logical Reasoning of Text","created":"2021-05-08","cited_by_count":46,"area":null},{"id":"2210.13865","title":"Missing Counter-Evidence Renders NLP Fact-Checking Unrealistic for Misinformation","created":"2022-10-25","cited_by_count":45,"area":null},{"id":"2311.09022","title":"Exploring the Potential of Large Language Models in Computational Argumentation","created":"2023-11-15","cited_by_count":42,"area":null},{"id":"2204.04541","title":"KOBEST: Korean Balanced Evaluation of Significant Tasks","created":"2022-04-09","cited_by_count":37,"area":null},{"id":"2402.10614","title":"Can LLMs Speak For Diverse People? Tuning LLMs via Debate to Generate Controllable Controversial Statements","created":"2024-02-16","cited_by_count":34,"area":null},{"id":"2104.07467","title":"Cross-Domain Label-Adaptive Stance Detection","created":"2021-04-15","cited_by_count":30,"area":null},{"id":"2202.13758","title":"Logical Fallacy Detection","created":"2022-02-28","cited_by_count":25,"area":null},{"id":"2107.00281","title":"Scientia Potentia Est -- On the Role of Knowledge in Computational Argumentation","created":"2021-07-01","cited_by_count":17,"area":null},{"id":"2406.03181","title":"Missci: Reconstructing Fallacies in Misrepresented Science","created":"2024-06-05","cited_by_count":14,"area":null},{"id":"2106.02725","title":"MultiOpEd: A Corpus of Multi-Perspective News Editorials","created":"2021-06-04","cited_by_count":13,"area":null},{"id":"2108.06665","title":"Accurate, yet inconsistent? Consistency Analysis on Language Understanding Models","created":"2021-08-15","cited_by_count":13,"area":null},{"id":"2402.10073","title":"Both Matter: Enhancing the Emotional Intelligence of Large Language Models without Compromising the General Intelligence","created":"2024-02-15","cited_by_count":11,"area":null},{"id":"2105.04505","title":"Towards Benchmarking the Utility of Explanations for Model Debugging","created":"2021-05-10","cited_by_count":10,"area":null},{"id":"2404.10259","title":"Uncovering Latent Arguments in Social Media Messaging by Employing LLMs-in-the-Loop Strategy","created":"2024-04-16","cited_by_count":9,"area":null},{"id":"2410.11672","title":"Leaving the barn door open for Clever Hans: Simple features predict LLM benchmark answers","created":"2024-10-15","cited_by_count":9,"area":null},{"id":"2110.01509","title":"DeepA2: A Modular Framework for Deep Argument Analysis with Pretrained Neural Text2Text Language Models","created":"2021-10-04","cited_by_count":7,"area":null},{"id":"2109.05358","title":"Implicit Premise Generation with Discourse-aware Commonsense Knowledge Models","created":"2021-09-11","cited_by_count":6,"area":null},{"id":"2209.02508","title":"\"Mama Always Had a Way of Explaining Things So I Could Understand'': A Dialogue Corpus for Learning to Construct Explanations","created":"2022-09-06","cited_by_count":6,"area":null},{"id":"2211.01874","title":"Contextual information integration for stance detection via cross-attention","created":"2022-11-03","cited_by_count":6,"area":null},{"id":"2311.02760","title":"Causal Question Answering with Reinforcement Learning","created":"2023-11-05","cited_by_count":5,"area":null},{"id":"2205.01523","title":"ElitePLM: An Empirical Study on General Language Ability Evaluation of Pretrained Language Models","created":"2022-05-03","cited_by_count":4,"area":null},{"id":"2306.00765","title":"Topic-Guided Sampling For Data-Efficient Multi-Domain Stance Detection","created":"2023-06-01","cited_by_count":4,"area":null},{"id":"2311.09761","title":"MAFALDA: A Benchmark and Comprehensive Study of Fallacy Detection and Classification","created":"2023-11-16","cited_by_count":4,"area":null},{"id":"2505.22076","title":"ArgInstruct: Specialized Instruction Fine-Tuning for Computational Argumentation","created":"2025-05-28","cited_by_count":4,"area":null},{"id":"2105.03157","title":"CO-NNECT: A Framework for Revealing Commonsense Knowledge Paths as Explicitations of Implicit Knowledge in Texts","created":"2021-05-07","cited_by_count":2,"area":null},{"id":"2109.06508","title":"Tribrid: Stance Classification with Neural Inconsistency Detection","created":"2021-09-14","cited_by_count":2,"area":null},{"id":"2204.01512","title":"LPAttack: A Feasible Annotation Scheme for Capturing Logic Pattern of Attacks in Arguments","created":"2022-04-04","cited_by_count":2,"area":null},{"id":"2310.02568","title":"Stand for Something or Fall for Everything: Predict Misinformation Spread with Stance-Aware Graph Neural Networks","created":"2023-10-04","cited_by_count":2,"area":null},{"id":"2403.00662","title":"Modeling the Quality of Dialogical Explanations","created":"2024-03-01","cited_by_count":2,"area":null},{"id":"2405.09439","title":"Facilitating Opinion Diversity through Hybrid NLP Approaches","created":"2024-05-15","cited_by_count":2,"area":null},{"id":"2508.03250","title":"RooseBERT: A New Deal For Political Language Modelling","created":"2025-08-05","cited_by_count":2,"area":null},{"id":"2603.06114","title":"Making Implicit Premises Explicit in Logical Understanding of Enthymemes","created":"2026-03-06","cited_by_count":2,"area":null},{"id":"2110.02054","title":"NoiER: An Approach for Training more Reliable Fine-TunedDownstream Task Models","created":"2021-08-29","cited_by_count":1,"area":null},{"id":"2404.03987","title":"Investigating the Robustness of Modelling Decisions for Few-Shot Cross-Topic Stance Detection: A Preregistered Study","created":"2024-04-05","cited_by_count":1,"area":null},{"id":"2411.04555","title":"An Axiomatic Study of the Evaluation of Enthymeme Decoding in Weighted Structured Argumentation","created":"2024-11-07","cited_by_count":1,"area":null},{"id":"2411.05192","title":"Explaining Mixtures of Sources in News Articles","created":"2024-11-07","cited_by_count":1,"area":null},{"id":"2604.17366","title":"ArgBench: Benchmarking LLMs on Computational Argumentation Tasks","created":"2026-04-19","cited_by_count":1,"area":null},{"id":"2608.18820","title":"Pairwise Logical Selection of Enthymeme Completions under Semantic-Link Uncertainty","created":"2026-08-19","cited_by_count":1,"area":null},{"id":"2102.04632","title":"Statistically Profiling Biases in Natural Language Reasoning Datasets and Models","created":"2021-02-09","cited_by_count":0,"area":null},{"id":"2111.05139","title":"Human-in-the-Loop Disinformation Detection: Stance, Sentiment, or Something Else?","created":"2021-11-09","cited_by_count":0,"area":null},{"id":"2206.07535","title":"BaIT: Barometer for Information Trustworthiness","created":"2022-06-15","cited_by_count":0,"area":null},{"id":"2307.15341","title":"Teach Me How to Improve My Argumentation Skills: A Survey on Feedback in Argumentation","created":"2023-07-28","cited_by_count":0,"area":null},{"id":"2606.12186","title":"A Resource for Enthymeme Detection in Controversial Political Discourse","created":"2026-06-10","cited_by_count":0,"area":null},{"id":"2608.18821","title":"Identifying Implicit Premises for Logical Reconstruction of Argument Graphs","created":"2026-08-19","cited_by_count":0,"area":null},{"id":"2610.17077","title":"UZHCL_for_the_win at Touche: Encoder Fine-Tuning, Synthetic Augmentation, and an Underpowered Encoder-LLM Comparison","created":"2026-10-12","cited_by_count":0,"area":null}],"next_cursor":null}