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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4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 4verdicts
UNVERDICTED 4representative citing papers
The paper proposes the BADx metric to quantify persona-induced amplification of implicit intersectional biases in five LLMs, showing that context modulates bias beyond what static embedding tests capture.
LLMs generate lower-quality STEM explanations for marginalized student profiles in Indian and American contexts, with intersectional compounding producing gaps of up to 2.55 grade levels.
LLMs handle skin tone emoji modifiers better than dedicated embedding models but display systemic disparities in sentiment and semantic consistency across tones.
citing papers explorer
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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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Invisible Influences: Investigating Implicit Intersectional Biases through Persona Engineering in Large Language Models
The paper proposes the BADx metric to quantify persona-induced amplification of implicit intersectional biases in five LLMs, showing that context modulates bias beyond what static embedding tests capture.
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Compounding Disadvantage: Auditing Intersectional Bias in LLM-Generated Explanations Across Indian and American STEM Education
LLMs generate lower-quality STEM explanations for marginalized student profiles in Indian and American contexts, with intersectional compounding producing gaps of up to 2.55 grade levels.
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Digital Skin, Digital Bias: Uncovering Tone-Based Biases in LLMs and Emoji Embeddings
LLMs handle skin tone emoji modifiers better than dedicated embedding models but display systemic disparities in sentiment and semantic consistency across tones.