Empirical audit of LAION-2B-en and LAION-2B-multi finds overrepresentation of young adults, White people, and males plus stereotypical emotion associations across two attribute classifiers.
Should chatgpt be biased? challenges and risks of bias in large language models
9 Pith papers cite this work. Polarity classification is still indexing.
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LOKI enables memory-free lifelong knowledge editing via HSIC-based dynamic layer selection and null-space constrained updates, reporting up to 14% higher average accuracy than prior methods.
GMRL-BD detects untrustworthy topic boundaries for black-box LLMs by combining bias-diffusion on a Wikipedia KG with multi-agent RL, supported by a released dataset labeling biases in models like Llama2 and Qwen2.
A two-dimensional persona simulation framework generates harmful content that is more challenging to detect and comparably diverse to human-curated datasets for robust evaluation of detection systems.
Guardian-as-an-Advisor prepends risk labels and explanations from a guardian model to queries, improving LLM safety compliance and reducing over-refusal while adding minimal compute overhead.
Survey organizes LLM trustworthiness into seven categories and 29 sub-categories, measures eight sub-categories on popular models, and finds that more aligned models generally score higher but with varying effectiveness.
An LLM semantic-matching framework for journal recommendation reports 40.23% Top-3 accuracy on 23,609 statistics articles from 49 journals without task-specific training.
The paper surveys data-centric strategies for foundation models in computational healthcare and supplies a curated list of related models and datasets.
The paper identifies social and ethical risks from unguarded use of general-purpose LLMs in Canadian newcomer settlement and advocates for AI literacy programs plus customized models with human oversight.
citing papers explorer
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Unmasking LAION-5B: Age, Gender, Race, and Emotion Biases in Large-Scale Image Datasets
Empirical audit of LAION-2B-en and LAION-2B-multi finds overrepresentation of young adults, White people, and males plus stereotypical emotion associations across two attribute classifiers.
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LOKI: Memory-Free Null-Space Constrained Lifelong Knowledge Editing
LOKI enables memory-free lifelong knowledge editing via HSIC-based dynamic layer selection and null-space constrained updates, reporting up to 14% higher average accuracy than prior methods.
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Can We Trust a Black-box LLM? LLM Untrustworthy Boundary Detection via Bias-Diffusion and Multi-Agent Reinforcement Learning
GMRL-BD detects untrustworthy topic boundaries for black-box LLMs by combining bias-diffusion on a Wikipedia KG with multi-agent RL, supported by a released dataset labeling biases in models like Llama2 and Qwen2.
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Beyond Static Benchmarks: Synthesizing Harmful Content via Persona-based Simulation for Robust Evaluation
A two-dimensional persona simulation framework generates harmful content that is more challenging to detect and comparably diverse to human-curated datasets for robust evaluation of detection systems.
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Guardian-as-an-Advisor: Advancing Next-Generation Guardian Models for Trustworthy LLMs
Guardian-as-an-Advisor prepends risk labels and explanations from a guardian model to queries, improving LLM safety compliance and reducing over-refusal while adding minimal compute overhead.
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Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment
Survey organizes LLM trustworthiness into seven categories and 29 sub-categories, measures eight sub-categories on popular models, and finds that more aligned models generally score higher but with varying effectiveness.
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An LLM-Powered Semantic Alignment Framework for Journal Recommendation
An LLM semantic-matching framework for journal recommendation reports 40.23% Top-3 accuracy on 23,609 statistics articles from 49 journals without task-specific training.
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Data-Centric Foundation Models in Computational Healthcare: A Survey
The paper surveys data-centric strategies for foundation models in computational healthcare and supplies a curated list of related models and datasets.
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Social and Ethical Risks Posed by General-Purpose LLMs for Settling Newcomers in Canada
The paper identifies social and ethical risks from unguarded use of general-purpose LLMs in Canadian newcomer settlement and advocates for AI literacy programs plus customized models with human oversight.