{"total":58,"rows":[{"id":"2502.14127","title":"Which of These Best Describes Multiple Choice Evaluation with LLMs? A) Forced B) Flawed C) Fixable D) All of the Above","created":"2025-02-19","cited_by_count":52,"area":null},{"id":"2409.08087","title":"Securing Large Language Models: Addressing Bias, Misinformation, and Prompt Attacks","created":"2024-09-12","cited_by_count":26,"area":"safety"},{"id":"2406.05392","title":"Deconstructing The Ethics of Large Language Models from Long-standing Issues to New-emerging Dilemmas: A Survey","created":"2024-06-08","cited_by_count":23,"area":null},{"id":"2406.03198","title":"The Impossibility of Fair LLMs","created":"2024-05-28","cited_by_count":22,"area":null},{"id":"2505.11454","title":"HumaniBench: A Human-Centric Framework for Large Multimodal Models Evaluation","created":"2025-05-16","cited_by_count":19,"area":"mllm"},{"id":"2407.02408","title":"CEB: Compositional Evaluation Benchmark for Fairness in Large Language Models","created":"2024-07-02","cited_by_count":18,"area":null},{"id":"2407.18745","title":"FairAIED: Navigating Fairness, Bias, and Ethics in Educational AI Applications","created":"2024-07-26","cited_by_count":15,"area":null},{"id":"2407.18454","title":"Fairness Definitions in Language Models Explained","created":"2024-07-26","cited_by_count":11,"area":null},{"id":"2411.00585","title":"Fairness Testing of Large Language Models in Role-Playing","created":"2024-11-01","cited_by_count":11,"area":null},{"id":"2504.02963","title":"Digital Forensics in the Age of Large Language Models","created":"2025-04-03","cited_by_count":10,"area":null},{"id":"2411.08243","title":"Beyond the Safety Bundle: Auditing the Helpful and Harmless Dataset","created":"2024-11-12","cited_by_count":8,"area":null},{"id":"2408.00992","title":"Fairness in Large Language Models in Three Hours","created":"2024-08-02","cited_by_count":7,"area":null},{"id":"2504.13199","title":"Building Trustworthy Multimodal AI: A Review of Fairness, Transparency, and Ethics in Vision-Language Tasks","created":"2025-04-14","cited_by_count":7,"area":"mllm"},{"id":"2408.11735","title":"Clinical Insights: A Comprehensive Review of Language Models in Medicine","created":"2024-08-21","cited_by_count":6,"area":null},{"id":"2412.04642","title":"Improving LLM Group Fairness on Tabular Data via In-Context Learning","created":"2024-12-05","cited_by_count":6,"area":null},{"id":"2508.15831","title":"Who's Asking? Investigating Bias Through the Lens of Disability Framed Queries in LLMs","created":"2025-08-18","cited_by_count":6,"area":null},{"id":"2507.07186","title":"Planted in Pretraining, Swayed by Finetuning: A Case Study on the Origins of Cognitive Biases in LLMs","created":"2025-07-09","cited_by_count":5,"area":null},{"id":"2510.09871","title":"CoBia: Constructed Conversations Can Trigger Otherwise Concealed Societal Biases in LLMs","created":"2025-10-10","cited_by_count":5,"area":"safety"},{"id":"2510.21887","title":"Generative AI in Depth: A Survey of Recent Advances, Model Variants, and Real-World Applications","created":"2025-10-23","cited_by_count":5,"area":"diffusion"},{"id":"2501.17310","title":"Probing LLM World Models: Enhancing Guesstimation with Wisdom of Crowds Decoding","created":"2025-01-28","cited_by_count":4,"area":"wm"},{"id":"2511.00382","title":"Efficiency vs. Alignment: Investigating Safety and Fairness Risks in Parameter-Efficient Fine-Tuning of LLMs","created":"2025-11-01","cited_by_count":4,"area":null},{"id":"2407.19655","title":"AI-Driven Healthcare: A Review on Ensuring Fairness and Mitigating Bias","created":"2024-07-29","cited_by_count":3,"area":null},{"id":"2503.07691","title":"Fair Text Classification via Transferable Representations","created":"2025-03-10","cited_by_count":3,"area":null},{"id":"2504.07516","title":"Enhancements for Developing a Comprehensive AI Fairness Assessment Standard","created":"2025-04-10","cited_by_count":3,"area":null},{"id":"2505.24262","title":"On Fairness of Task Arithmetic: The Role of Task Vectors","created":"2025-05-30","cited_by_count":3,"area":null},{"id":"2506.05390","title":"Understanding Gender Bias in AI-Generated Product Descriptions","created":"2025-06-03","cited_by_count":3,"area":null},{"id":"2509.00641","title":"AMCR: A Framework for Assessing and Mitigating Copyright Risks in Generative Models","created":"2025-08-31","cited_by_count":3,"area":null},{"id":"2509.21631","title":"Towards Transparent AI: A Survey on Explainable Language Models","created":"2025-09-25","cited_by_count":3,"area":null},{"id":"2407.21202","title":"Rolling in the deep of cognitive and AI biases","created":"2024-07-30","cited_by_count":2,"area":null},{"id":"2505.09289","title":"Reproducibility Study of \"Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents\"","created":"2025-05-14","cited_by_count":2,"area":"agents"},{"id":"2505.23996","title":"Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for LLMs","created":"2025-05-29","cited_by_count":2,"area":null},{"id":"2510.02128","title":"The Disparate Impacts of Speculative Decoding","created":"2025-10-02","cited_by_count":2,"area":"inference"},{"id":"2602.00044","title":"When LLMs Imagine People: A Human-Centered Persona Brainstorm Audit for Bias and Fairness in Creative Applications","created":"2026-01-19","cited_by_count":2,"area":null},{"id":"2411.14500","title":"Exploring Accuracy-Fairness Trade-off in Large Language Models","created":"2024-11-21","cited_by_count":1,"area":null},{"id":"2504.01951","title":"The LLM Wears Prada: Analysing Gender Bias and Stereotypes through Online Shopping Data","created":"2025-04-02","cited_by_count":1,"area":null},{"id":"2506.11244","title":"Iterative Multilingual Spectral Attribute Erasure","created":"2025-06-12","cited_by_count":1,"area":null},{"id":"2510.04528","title":"Unified Threat Detection and Mitigation Framework (UTDMF): Combating Prompt Injection, Deception, and Bias in Enterprise-Scale Transformers","created":"2025-10-06","cited_by_count":1,"area":"safety"},{"id":"2511.13540","title":"Fairness-Aware Graph Representation Learning with Limited Demographic Information","created":"2025-11-17","cited_by_count":1,"area":null},{"id":"2601.20704","title":"Structurally Human, Semantically Biased: Detecting LLM-Generated References with Embeddings and GNNs","created":"2026-01-28","cited_by_count":1,"area":null},{"id":"2501.12962","title":"It's complicated. The relationship of algorithmic fairness and non-discrimination provisions for high-risk systems in the EU AI Act","created":"2025-01-22","cited_by_count":0,"area":null},{"id":"2504.12323","title":"The Other Side of the Coin: Exploring Fairness in Retrieval-Augmented Generation","created":"2025-04-11","cited_by_count":0,"area":"rag"},{"id":"2505.12100","title":"Improving Fairness in LLMs Through Testing-Time Adversaries","created":"2025-05-17","cited_by_count":0,"area":null},{"id":"2507.04224","title":"Fairness Evaluation of Large Language Models in Academic Library Reference Services","created":"2025-07-06","cited_by_count":0,"area":null},{"id":"2507.09826","title":"Bridging Neural Networks and Dynamic Time Warping for Adaptive Time Series Classification","created":"2025-07-13","cited_by_count":0,"area":null},{"id":"2508.07753","title":"Exploring Causal Effect of Social Bias on Faithfulness Hallucinations in Large Language Models","created":"2025-08-11","cited_by_count":0,"area":null},{"id":"2508.08151","title":"FairFLRep: Fairness aware fault localization and repair of Deep Neural Networks","created":"2025-08-11","cited_by_count":0,"area":null},{"id":"2508.09651","title":"A Close Reading Approach to Gender Narrative Biases in AI-Generated Stories","created":"2025-08-13","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":"2508.18466","title":"Integrating gender inclusivity into large language models via instruction tuning","created":"2025-08-25","cited_by_count":0,"area":null},{"id":"2509.13651","title":"Controllable Pareto Trade-off between Fairness and Accuracy","created":"2025-09-17","cited_by_count":0,"area":null},{"id":"2512.00556","title":"Bias Testing and Mitigation in Black Box LLMs using Metamorphic Relations","created":"2025-11-29","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":"2602.04408","title":"Separation-Utility Pareto Frontier: An Information-Theoretic Characterization","created":"2026-02-04","cited_by_count":0,"area":null},{"id":"2605.08671","title":"Explanation Fairness in Large Language Models: An Empirical Analysis of Disparities in How LLMs Justify Decisions Across Demographic Groups","created":"2026-05-09","cited_by_count":0,"area":null},{"id":"2605.20191","title":"Shiny Stories, Hidden Struggles: Investigating the Representation of Disability Through the Lens of LLMs","created":"2026-04-02","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":"2608.08284","title":"Fair on the Surface? Benchmarking Hidden-Output Fairness Gaps in LLM Recommenders","created":"2026-08-08","cited_by_count":0,"area":null},{"id":"2609.21296","title":"FairLMs: A Turnkey Library for Fairness in Language Models","created":"2026-09-18","cited_by_count":0,"area":null}],"next_cursor":null}