AI & ML interests
None defined yet.
Recent Activity
Benchmarking the Robustness of Image Watermarks. Under development. Data will be released soon.
Automatic Pseudo-Harmful Prompt Generation for Evaluating False Refusals in Large Language Models
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furonghuang-lab/PHTest
Viewer • Updated • 3.27k • 82 • 3 -
AutoDAN: Interpretable Gradient-Based Adversarial Attacks on Large Language Models
Paper • 2310.15140 • Published • 1 -
Automatic Pseudo-Harmful Prompt Generation for Evaluating False Refusals in Large Language Models
Paper • 2409.00598 • Published
TraceVLA: Visual Trace Prompting Enhances Spatial-Temporal Awareness for Generalist Robotic Policies
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TraceVLA: Visual Trace Prompting Enhances Spatial-Temporal Awareness for Generalist Robotic Policies
Paper • 2412.10345 • Published • 2 -
furonghuang-lab/tracevla_phi3v
Text Generation • Updated • 14 -
furonghuang-lab/tracevla_7b
Text Generation • 8B • Updated • 24 -
furonghuang-lab/openvla_phi3v
Text Generation • Updated
Easy2Hard-Bench offers six datasets with continuous difficulty ratings, enabling profiling of LLM performance and generalization across difficulties.
TraceVLA: Visual Trace Prompting Enhances Spatial-Temporal Awareness for Generalist Robotic Policies
-
TraceVLA: Visual Trace Prompting Enhances Spatial-Temporal Awareness for Generalist Robotic Policies
Paper • 2412.10345 • Published • 2 -
furonghuang-lab/tracevla_phi3v
Text Generation • Updated • 14 -
furonghuang-lab/tracevla_7b
Text Generation • 8B • Updated • 24 -
furonghuang-lab/openvla_phi3v
Text Generation • Updated
Benchmarking the Robustness of Image Watermarks. Under development. Data will be released soon.
Easy2Hard-Bench offers six datasets with continuous difficulty ratings, enabling profiling of LLM performance and generalization across difficulties.
Automatic Pseudo-Harmful Prompt Generation for Evaluating False Refusals in Large Language Models
-
furonghuang-lab/PHTest
Viewer • Updated • 3.27k • 82 • 3 -
AutoDAN: Interpretable Gradient-Based Adversarial Attacks on Large Language Models
Paper • 2310.15140 • Published • 1 -
Automatic Pseudo-Harmful Prompt Generation for Evaluating False Refusals in Large Language Models
Paper • 2409.00598 • Published