{"total":92,"rows":[{"id":"2310.11986","title":"Sociotechnical Safety Evaluation of Generative AI Systems","created":"2023-10-18","cited_by_count":157,"area":null},{"id":"2402.04614","title":"Faithfulness vs. Plausibility: On the (Un)Reliability of Explanations from Large Language Models","created":"2024-02-07","cited_by_count":105,"area":null},{"id":"2403.14117","title":"A Design Space for Intelligent and Interactive Writing Assistants","created":"2024-03-21","cited_by_count":76,"area":null},{"id":"2309.15098","title":"Attention Satisfies: A Constraint-Satisfaction Lens on Factual Errors of Language Models","created":"2023-09-26","cited_by_count":67,"area":null},{"id":"2405.00623","title":"\"I'm Not Sure, But...\": Examining the Impact of Large Language Models' Uncertainty Expression on User Reliance and Trust","created":"2024-05-01","cited_by_count":67,"area":null},{"id":"2307.15703","title":"Uncertainty in Natural Language Generation: From Theory to Applications","created":"2023-07-28","cited_by_count":61,"area":null},{"id":"2402.17385","title":"Determinants of LLM-assisted Decision-Making","created":"2024-02-27","cited_by_count":56,"area":null},{"id":"2410.16491","title":"BIG5-CHAT: Shaping LLM Personalities Through Training on Human-Grounded Data","created":"2024-10-21","cited_by_count":50,"area":null},{"id":"2402.02713","title":"Position: What Can Large Language Models Tell Us about Time Series Analysis","created":"2024-02-05","cited_by_count":47,"area":null},{"id":"2405.02079","title":"Argumentative Large Language Models for Explainable and Contestable Claim Verification","created":"2024-05-03","cited_by_count":36,"area":null},{"id":"2311.05014","title":"Interpreting Pretrained Language Models via Concept Bottlenecks","created":"2023-11-08","cited_by_count":33,"area":null},{"id":"2405.10313","title":"How Far Are We From AGI: Are LLMs All We Need?","created":"2024-05-16","cited_by_count":31,"area":null},{"id":"2402.15350","title":"Farsight: Fostering Responsible AI Awareness During AI Application Prototyping","created":"2024-02-23","cited_by_count":28,"area":null},{"id":"2406.18841","title":"Navigating LLM Ethics: Advancements, Challenges, and Future Directions","created":"2024-05-14","cited_by_count":28,"area":null},{"id":"2402.08030","title":"Why and When LLM-Based Assistants Can Go Wrong: Investigating the Effectiveness of Prompt-Based Interactions for Software Help-Seeking","created":"2024-02-12","cited_by_count":26,"area":null},{"id":"2407.15248","title":"XAI meets LLMs: A Survey of the Relation between Explainable AI and Large Language Models","created":"2024-07-21","cited_by_count":23,"area":null},{"id":"2404.09554","title":"Explainable Generative AI (GenXAI): A Survey, Conceptualization, and Research Agenda","created":"2024-04-15","cited_by_count":22,"area":null},{"id":"2407.13934","title":"Towards Trustworthy AI: A Review of Ethical and Robust Large Language Models","created":"2024-06-01","cited_by_count":22,"area":null},{"id":"2506.07461","title":"From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered","created":"2025-06-09","cited_by_count":21,"area":null},{"id":"2404.04671","title":"PhyloLM : Inferring the Phylogeny of Large Language Models and Predicting their Performances in Benchmarks","created":"2024-04-06","cited_by_count":19,"area":null},{"id":"2412.12004","title":"The Open Source Advantage in Large Language Models (LLMs)","created":"2024-12-16","cited_by_count":18,"area":null},{"id":"2402.01705","title":"Beyond Behaviorist Representational Harms: A Plan for Measurement and Mitigation","created":"2024-01-25","cited_by_count":17,"area":null},{"id":"2310.10706","title":"Harnessing the Power of LLMs: Evaluating Human-AI Text Co-Creation through the Lens of News Headline Generation","created":"2023-10-16","cited_by_count":14,"area":null},{"id":"2410.11042","title":"Persistent Topological Features in Large Language Models","created":"2024-10-14","cited_by_count":12,"area":null},{"id":"2403.08950","title":"Exploring Prompt Engineering Practices in the Enterprise","created":"2024-03-13","cited_by_count":11,"area":null},{"id":"2407.08983","title":"Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanations","created":"2024-07-12","cited_by_count":11,"area":null},{"id":"2406.02524","title":"CheckEmbed: Effective Verification of LLM Solutions to Open-Ended Tasks","created":"2024-06-04","cited_by_count":10,"area":null},{"id":"2405.20434","title":"Facilitating Human-LLM Collaboration through Factuality Scores and Source Attributions","created":"2024-05-30","cited_by_count":9,"area":null},{"id":"2409.19450","title":"Secret Use of Large Language Model (LLM)","created":"2024-09-28","cited_by_count":9,"area":null},{"id":"2308.09004","title":"Towards Lightweight Data Integration using Multi-workflow Provenance and Data Observability","created":"2023-08-17","cited_by_count":8,"area":null},{"id":"2412.11656","title":"Private Yet Social: How LLM Chatbots Support and Challenge Eating Disorder Recovery","created":"2024-12-16","cited_by_count":8,"area":null},{"id":"2502.13780","title":"Translation in the Hands of Many:Centering Lay Users in Machine Translation Interactions","created":"2025-02-19","cited_by_count":8,"area":null},{"id":"2409.15436","title":"Ads that Talk Back: Implications and Perceptions of Injecting Personalized Advertising into LLM Chatbots","created":"2024-09-23","cited_by_count":7,"area":null},{"id":"2503.11827","title":"Bridging the LLM Accessibility Divide? Performance, Fairness, and Cost of Closed versus Open LLMs for Automated Essay Scoring","created":"2025-03-14","cited_by_count":7,"area":null},{"id":"2403.04760","title":"iScore: Visual Analytics for Interpreting How Language Models Automatically Score Summaries","created":"2024-03-07","cited_by_count":6,"area":null},{"id":"2502.13321","title":"Adjust for Trust: Mitigating Trust-Induced Inappropriate Reliance on AI Assistance","created":"2025-02-18","cited_by_count":6,"area":null},{"id":"2306.05809","title":"Interactive Explanation with Varying Level of Details in an Explainable Scientific Literature Recommender System","created":"2023-06-09","cited_by_count":5,"area":null},{"id":"2310.01297","title":"Co-audit: tools to help humans double-check AI-generated content","created":"2023-10-02","cited_by_count":5,"area":null},{"id":"2504.15549","title":"Do It For Me vs. Do It With Me: Investigating User Perceptions of Different Paradigms of Automation in Copilots for Feature-Rich Software","created":"2025-04-22","cited_by_count":5,"area":null},{"id":"2608.23642","title":"AI Agents Push Humans Out of the Loop","created":"2026-08-24","cited_by_count":5,"area":null},{"id":"2310.07715","title":"To Build Our Future, We Must Know Our Past: Contextualizing Paradigm Shifts in Natural Language Processing","created":"2023-10-11","cited_by_count":4,"area":null},{"id":"2406.10632","title":"Applications of Generative AI in Healthcare: algorithmic, ethical, legal and societal considerations","created":"2024-06-15","cited_by_count":4,"area":null},{"id":"2410.18906","title":"PRISM: A Methodology for Auditing Biases in Large Language Models","created":"2024-10-24","cited_by_count":4,"area":null},{"id":"2410.22177","title":"Analyzing Multimodal Interaction Strategies for LLM-Assisted Manipulation of 3D Scenes","created":"2024-10-29","cited_by_count":4,"area":null},{"id":"2603.18976","title":"Evaluating 5W3H Structured Prompting for Intent Alignment in Human-AI Interaction","created":"2026-03-19","cited_by_count":4,"area":null},{"id":"2310.07802","title":"An Information Bottleneck Characterization of the Understanding-Workload Tradeoff","created":"2023-10-11","cited_by_count":3,"area":null},{"id":"2311.11211","title":"Leveraging Generative AI for Clinical Evidence Summarization Needs to Ensure Trustworthiness","created":"2023-11-19","cited_by_count":3,"area":null},{"id":"2501.10909","title":"Fine-Grained Appropriate Reliance: Human-AI Collaboration with a Multi-Step Transparent Decision Workflow for Complex Task Decomposition","created":"2025-01-19","cited_by_count":3,"area":null},{"id":"2502.19410","title":"Less or More: Towards Glanceable Explanations for LLM Recommendations Using Ultra-Small Devices","created":"2025-02-26","cited_by_count":3,"area":null},{"id":"2603.25379","title":"Does Structured Intent Representation Generalize? A Cross-Language, Cross-Model Empirical Study of 5W3H Prompting","created":"2026-03-26","cited_by_count":3,"area":null},{"id":"2409.07423","title":"Enhancing adversarial robustness in Natural Language Inference using explanations","created":"2024-09-11","cited_by_count":2,"area":null},{"id":"2411.15396","title":"The Decoy Dilemma in Online Medical Information Evaluation: A Comparative Study of Credibility Assessments by LLM and Human Judges","created":"2024-11-23","cited_by_count":2,"area":null},{"id":"2510.01255","title":"Longitudinal Monitoring of LLM Content Moderation of Social Issues","created":"2025-09-24","cited_by_count":2,"area":null},{"id":"2602.19844","title":"LLM-enabled Applications Require System-Level Threat Monitoring","created":"2026-02-23","cited_by_count":2,"area":null},{"id":"2602.21045","title":"PaperTrail: A Claim-Evidence Interface for Grounding Provenance in LLM-based Scholarly Q&A","created":"2026-02-24","cited_by_count":2,"area":null},{"id":"2603.29953","title":"Structured Intent as a Protocol-Like Communication Layer: Cross-Model Robustness, Framework Comparison, and the Weak-Model Compensation Effect","created":"2026-03-31","cited_by_count":2,"area":null},{"id":"2605.06901","title":"Reflections and New Directions for Human-Centered Large Language Models","created":"2026-05-07","cited_by_count":2,"area":null},{"id":"2406.06574","title":"Towards Transparency: Exploring LLM Trainings Datasets through Visual Topic Modeling and Semantic Frame","created":"2024-06-03","cited_by_count":1,"area":null},{"id":"2411.02594","title":"A Risk Taxonomy and Reflection Tool for Large Language Model Adoption in Public Health","created":"2024-11-04","cited_by_count":1,"area":null},{"id":"2502.16701","title":"Beyond Release: Access Considerations for Generative AI Systems","created":"2025-02-23","cited_by_count":1,"area":null},{"id":"2503.15511","title":"The Trust Calibration Maturity Model for Characterizing and Communicating Trustworthiness of AI Systems","created":"2025-01-28","cited_by_count":1,"area":null},{"id":"2504.09961","title":"Privacy Meets Explainability: Managing Confidential Data and Transparency Policies in LLM-Empowered Science","created":"2025-04-14","cited_by_count":1,"area":null},{"id":"2510.01220","title":"Towards Open-Ended Discovery for Low-Resource NLP","created":"2025-09-22","cited_by_count":1,"area":null},{"id":"2511.07277","title":"Designing Beyond Language: Sociotechnical Barriers in AI Health Technologies for Limited English Proficiency","created":"2025-11-10","cited_by_count":1,"area":null},{"id":"2512.12413","title":"Understanding Critical Thinking in Generative Artificial Intelligence Use: Development, Validation, and Correlates of the Critical Thinking in AI Use Scale","created":"2025-12-13","cited_by_count":1,"area":null},{"id":"2512.15979","title":"OLAF: Towards Robust LLM-Based Annotation Framework in Empirical Software Engineering","created":"2025-12-17","cited_by_count":1,"area":null},{"id":"2602.13248","title":"X-Blocks: Linguistic Building Blocks of Natural Language Explanations for Automated Vehicles","created":"2026-02-02","cited_by_count":1,"area":null},{"id":"2602.21059","title":"An Expert Schema for Evaluating Large Language Model Errors in Scholarly Question-Answering Systems","created":"2026-02-24","cited_by_count":1,"area":null},{"id":"2603.00078","title":"Alignment Is Not Enough: A Relational Framework for Moral Standing in Human-AI Interaction","created":"2026-02-13","cited_by_count":1,"area":null},{"id":"2603.18895","title":"From Accuracy to Readiness: Metrics and Benchmarks for Human-AI Decision-Making","created":"2026-03-19","cited_by_count":1,"area":null},{"id":"2604.17843","title":"Learning from AVA: Early Lessons from a Curated and Trustworthy Generative AI for Policy and Development Research","created":"2026-04-20","cited_by_count":1,"area":null},{"id":"2605.05767","title":"Priming, Path-dependence, and Plasticity: Understanding the molding of user-LLM interaction and its implications from (many) chat logs in the wild","created":"2026-05-07","cited_by_count":1,"area":null},{"id":"2410.22663","title":"Automated Trustworthiness Oracle Generation for Machine Learning Text Classifiers","created":"2024-10-30","cited_by_count":0,"area":null},{"id":"2411.01956","title":"EXAGREE: Mitigating Explanation Disagreement with Stakeholder-Aligned Models","created":"2024-11-04","cited_by_count":0,"area":null},{"id":"2503.13793","title":"Mapping the Trust Terrain: LLMs in Software Engineering -- Insights and Perspectives","created":"2025-03-18","cited_by_count":0,"area":null},{"id":"2503.15514","title":"Superhuman Game AI Disclosure: Expertise and Context Moderate Effects on Trust and Fairness","created":"2025-01-31","cited_by_count":0,"area":null},{"id":"2503.16466","title":"ACE, Action and Control via Explanations: A Proposal for LLMs to Provide Human-Centered Explainability for Multimodal AI Assistants","created":"2025-02-27","cited_by_count":0,"area":null},{"id":"2505.07178","title":"Accountability of Generative AI: Exploring a Precautionary Approach for \"Artificially Created Nature\"","created":"2025-05-12","cited_by_count":0,"area":null},{"id":"2508.06846","title":"Highlight All the Phrases: Enhancing LLM Transparency through Visual Factuality Indicators","created":"2025-08-09","cited_by_count":0,"area":null},{"id":"2602.11924","title":"Who Does What? Archetypes of Roles Assigned to LLMs During Human-AI Decision-Making","created":"2026-02-12","cited_by_count":0,"area":null},{"id":"2602.16140","title":"Human-AI Collaboration in Large Language Model-Integrated Building Energy Management Systems: The Role of User Domain Knowledge and AI Literacy","created":"2026-02-18","cited_by_count":0,"area":null},{"id":"2603.13294","title":"Real-World AI Evaluation: How FRAME Generates Systematic Evidence to Resolve the Decision-Maker's Dilemma","created":"2026-02-28","cited_by_count":0,"area":null},{"id":"2603.28306","title":"Self++: Co-Determined Agency for Human--AI Symbiosis in Extended Reality","created":"2026-03-30","cited_by_count":0,"area":null},{"id":"2604.06385","title":"Application-Driven Pedagogical Knowledge Optimization of Open-Source LLMs via Reinforcement Learning and Supervised Fine-Tuning","created":"2026-04-07","cited_by_count":0,"area":null},{"id":"2604.07121","title":"Mixed-Initiative Context: Structuring and Managing Context for Human-AI Collaboration","created":"2026-04-08","cited_by_count":0,"area":null},{"id":"2605.02765","title":"U-Define: Designing User Workflows for Hard and Soft Constraints in LLM-Based Planning","created":"2026-05-04","cited_by_count":0,"area":null},{"id":"2608.08830","title":"PROSLEX: A Novel Dataset for Expert-Annotated Legal Statute Prediction for Indian Judiciary","created":"2026-08-09","cited_by_count":0,"area":null},{"id":"2608.13622","title":"ARC: Fair Relative Advantage Comparison in Open-Ended Real-World Interaction","created":"2026-08-13","cited_by_count":0,"area":null},{"id":"2608.30543","title":"Designing an Auditable LLM-Supported Workflow for Qualitative Thematic Analysis","created":"2026-08-31","cited_by_count":0,"area":null},{"id":"2609.19151","title":"What Users Think of Generative AI: A Cross-Platform NLP Analysis of Trust and Friction in App Store Reviews","created":"2026-07-20","cited_by_count":0,"area":null},{"id":"2610.02631","title":"Designing the Future of User Feedback for Generative AI","created":"2026-10-02","cited_by_count":0,"area":null},{"id":"2610.16042","title":"AI Representations of Mental Health Stigma: Quantifying the Gap Between AI Estimations and Human Perceptions Across Gender Groups Using GPT-Based LLMs","created":"2026-10-12","cited_by_count":0,"area":null}],"next_cursor":null}