Closing the Distribution Gap in Adversarial Training for LLMs | ScienceToStartup | ScienceToStartup

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References (47)

[1]

AdversariaLLM: A Unified and Modular Toolbox for LLM Robustness Research

2025Tim Beyer, Jonas Dornbusch et al.

[2]

Diffusion LLMs are Natural Adversaries for any LLM

2025David Ludke, Tom Wollschlager et al.

[3]

Sampling-aware Adversarial Attacks Against Large Language Models

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"Enhance LLM robustness against adversaries using Distributional Adversarial Training with diffusion models."

LLM Training•Score: 2•View PDF ↗

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Closing the Distribution Gap in Adversarial Training for LLMs

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References (47)

Founder's Pitch

"Enhance LLM robustness against adversaries using Distributional Adversarial Training with diffusion models."

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Closing the Distribution Gap in Adversarial Training for LLMs

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References (47)

Founder's Pitch

"Enhance LLM robustness against adversaries using Distributional Adversarial Training with diffusion models."

Commercial Viability Breakdown

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