{"total":41,"rows":[{"id":"2407.00128","title":"When Search Engine Services meet Large Language Models: Visions and Challenges","created":"2024-06-28","cited_by_count":59,"area":null},{"id":"2409.16430","title":"A Comprehensive Survey of Bias in LLMs: Current Landscape and Future Directions","created":"2024-09-24","cited_by_count":27,"area":null},{"id":"2501.04437","title":"Integrating LLMs with ITS: Recent Advances, Potentials, Challenges, and Future Directions","created":"2025-01-08","cited_by_count":23,"area":null},{"id":"2406.10773","title":"Quantifying Generative Media Bias with a Corpus of Real-world and Generated News Articles","created":"2024-06-16","cited_by_count":9,"area":null},{"id":"2503.02457","title":"Don't Get Too Excited -- Eliciting Emotions in LLMs","created":"2025-03-04","cited_by_count":9,"area":null},{"id":"2408.04643","title":"Risks, Causes, and Mitigations of Widespread Deployments of Large Language Models (LLMs): A Survey","created":"2024-08-01","cited_by_count":8,"area":null},{"id":"2604.03976","title":"Quantifying Trust: Financial Risk Management for Trustworthy AI Agents","created":"2026-04-05","cited_by_count":8,"area":null},{"id":"2506.06751","title":"Geopolitical biases in LLMs: what are the \"good\" and the \"bad\" countries according to contemporary language models","created":"2025-06-07","cited_by_count":6,"area":null},{"id":"2410.05401","title":"Post-hoc Study of Climate Microtargeting on Social Media Ads with LLMs: Thematic Insights and Fairness Evaluation","created":"2024-10-07","cited_by_count":4,"area":null},{"id":"2504.03520","title":"Neutralizing the Narrative: AI-Powered Debiasing of Online News Articles","created":"2025-04-04","cited_by_count":4,"area":null},{"id":"2601.17172","title":"Who Gets Which Message? Auditing Demographic Bias in LLM-Generated Targeted Text","created":"2026-01-23","cited_by_count":4,"area":null},{"id":"2509.08146","title":"Bias after Prompting: Persistent Discrimination in Large Language Models","created":"2025-09-09","cited_by_count":3,"area":null},{"id":"2602.22475","title":"Mind the Gap in Cultural Alignment: Task-Aware Culture Management for Large Language Models","created":"2026-02-25","cited_by_count":3,"area":null},{"id":"2409.02375","title":"How Privacy-Savvy Are Large Language Models? A Case Study on Compliance and Privacy Technical Review","created":"2024-09-04","cited_by_count":2,"area":null},{"id":"2505.17131","title":"Relative Bias: A Comparative Framework for Quantifying Bias in LLMs","created":"2025-05-22","cited_by_count":2,"area":null},{"id":"2510.26899","title":"How Similar Are Grokipedia and Wikipedia? A Multi-Dimensional Textual and Structural Comparison","created":"2025-10-30","cited_by_count":2,"area":null},{"id":"2603.19297","title":"CLaRE-ty Amid Chaos: Quantifying Representational Entanglement to Predict Ripple Effects in LLM Editing","created":"2026-03-11","cited_by_count":2,"area":null},{"id":"2409.04043","title":"Towards Safer Online Spaces: Simulating and Assessing Intervention Strategies for Eating Disorder Discussions","created":"2024-09-06","cited_by_count":1,"area":null},{"id":"2410.12010","title":"Bias Similarity Measurement: A Black-Box Audit of Fairness Across LLMs","created":"2024-10-15","cited_by_count":1,"area":null},{"id":"2410.20490","title":"Who Speaks Matters: Analysing the Influence of the Speaker's Ethnicity on Hate Classification","created":"2024-10-27","cited_by_count":1,"area":null},{"id":"2510.25805","title":"Ideology-Based LLMs for Content Moderation","created":"2025-10-29","cited_by_count":1,"area":null},{"id":"2608.12113","title":"Structuring the Space of Perspectives","created":"2026-08-12","cited_by_count":1,"area":null},{"id":"2503.16674","title":"Through the LLM Looking Glass: A Socratic Probing of Donkeys, Elephants, and Markets","created":"2025-03-20","cited_by_count":0,"area":null},{"id":"2508.11061","title":"BIPOLAR: Polarization-based granular framework for LLM bias evaluation","created":"2025-08-14","cited_by_count":0,"area":null},{"id":"2508.15855","title":"Counterspeech for Mitigating the Influence of Media Bias: Comparing Human and LLM-Generated Responses","created":"2025-08-20","cited_by_count":0,"area":null},{"id":"2509.26415","title":"Automatic Fact-checking in English and Telugu","created":"2025-09-30","cited_by_count":0,"area":null},{"id":"2510.16206","title":"The Right to Be Remembered: Preserving Maximally Truthful Digital Memory in the Age of AI","created":"2025-10-17","cited_by_count":0,"area":null},{"id":"2511.19785","title":"Gender Bias in Emotion Recognition by Large Language Models","created":"2025-11-24","cited_by_count":0,"area":null},{"id":"2512.10734","title":"Textual Data Bias Detection and Mitigation -- An Extensible Pipeline with Experimental Evaluation","created":"2025-12-11","cited_by_count":0,"area":null},{"id":"2601.17016","title":"Measuring Political Stance and Consistency in Large Language Models","created":"2026-01-15","cited_by_count":0,"area":null},{"id":"2602.08740","title":"Map of Encoders -- Mapping Sentence Encoders using Quantum Relative Entropy","created":"2026-02-09","cited_by_count":0,"area":null},{"id":"2604.19431","title":"Counting Worlds Branching Time Semantics for post-hoc Bias Mitigation in generative AI","created":"2026-04-21","cited_by_count":0,"area":null},{"id":"2604.22089","title":"Ethics Testing: Proactive Identification of Generative AI System Harms","created":"2026-04-23","cited_by_count":0,"area":null},{"id":"2604.27661","title":"Language Ideologies in a Multilingual Society: An LLM-based Analysis of Luxembourgish News Comments","created":"2026-04-30","cited_by_count":0,"area":null},{"id":"2606.01282","title":"KG-FairDiff: Knowledge Graph-Guided Prompt Refinement for Demographically Fair Text-to-Image Generation","created":"2026-05-31","cited_by_count":0,"area":null},{"id":"2606.17506","title":"Evaluating Second-Order Bias of LLMs Through Epistemic Entitlement","created":"2026-06-16","cited_by_count":0,"area":null},{"id":"2606.27382","title":"AI-Model Network: Concept, Current State and Future","created":"2026-05-25","cited_by_count":0,"area":null},{"id":"2607.05574","title":"Whose fairness? Structural concentration in AI bias research","created":"2026-07-06","cited_by_count":0,"area":null},{"id":"2607.15957","title":"From Plausible to Actionable: A Position on LLM Self-Explanations","created":"2026-07-17","cited_by_count":0,"area":null},{"id":"2608.21928","title":"GuardianBench: A Same-Scene Instruction-Contrastive Benchmark for Latent Contextual Risk in Embodied AI","created":"2026-08-22","cited_by_count":0,"area":null},{"id":"2609.10060","title":"Reference-Based Bias Detection in LLMs via Relative Representations of Hidden States","created":"2026-09-09","cited_by_count":0,"area":null}],"next_cursor":null}