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Found 11 papers, 3 papers with code
Leveraging Prior Experience: An Expandable Auxiliary Knowledge Base for Text-to-SQL
Large Language Models (LLMs) exhibit impressive problem-solving skills across many tasks, but they still underperform compared to humans in various downstream applications, such as text-to-SQL.
Fairness in Large Language Models in Three Hours
Large Language Models (LLMs) have demonstrated remarkable success across various domains but often lack fairness considerations, potentially leading to discriminatory outcomes against marginalized populations.
AI-Driven Healthcare: A Survey on Ensuring Fairness and Mitigating Bias
Artificial intelligence (AI) is rapidly advancing in healthcare, enhancing the efficiency and effectiveness of services across various specialties, including cardiology, ophthalmology, dermatology, emergency medicine, etc.
Fairness Definitions in Language Models Explained
Specifically, we begin with a brief introduction to LMs and fairness in LMs, followed by a comprehensive, up-to-date overview of existing fairness notions in LMs and the introduction of a novel taxonomy that categorizes these concepts based on their foundational principles and operational distinctions.
Fairness in Large Language Models: A Taxonomic Survey
Additionally, the concept of fairness in LLMs is discussed categorically, summarizing metrics for evaluating bias in LLMs and existing algorithms for promoting fairness.
Uncertain Boundaries: Multidisciplinary Approaches to Copyright Issues in Generative AI
In the rapidly evolving landscape of generative artificial intelligence (AI), the increasingly pertinent issue of copyright infringement arises as AI advances to generate content from scraped copyrighted data, prompting questions about ownership and protection that impact professionals across various careers.
History, Development, and Principles of Large Language Models-An Introductory Survey
Language models serve as a cornerstone in natural language processing (NLP), utilizing mathematical methods to generalize language laws and knowledge for prediction and generation.
Towards Fair Machine Learning Software: Understanding and Addressing Model Bias Through Counterfactual Thinking
The increasing use of Machine Learning (ML) software can lead to unfair and unethical decisions, thus fairness bugs in software are becoming a growing concern.
Preventing Discriminatory Decision-making in Evolving Data Streams
However, most fair machine learning (fair-ML) work to address bias in decision-making systems has focused solely on the offline setting.
Individual Fairness under Uncertainty
Algorithmic fairness, the research field of making machine learning (ML) algorithms fair, is an established area in ML.
