Green Shielding introduces CUE criteria and the HCM-Dx benchmark to demonstrate that routine prompt variations systematically alter LLM diagnostic behavior along clinically relevant dimensions, producing Pareto-like tradeoffs in plausibility versus coverage.
The Order Effect: Investigating Prompt Sensitivity to Input Order in LLMs
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 5verdicts
UNVERDICTED 5roles
background 1polarities
background 1representative citing papers
S2H-DPO generates hierarchical prompt-driven preference pairs to improve multi-image reasoning in VLMs while keeping single-image performance intact.
LLMs fail to reliably detect culturally embedded health misinformation on YouTube because promotional and debunking content share similar rhetorical registers that blend tradition with pseudo-science, and this limitation persists across prompt variations.
Fine-tuning Qwen-Math-7B with LoRA and GRPO on BlueSky simulator data improves LLM accuracy and consistency in cooperative sUAS tactical deconfliction, reducing near mid-air collisions.
Evaluation shows LLMs for healthcare are sensitive to prompt changes, leading to inconsistent and potentially harmful clinical outputs on MedMCQA.
citing papers explorer
-
Green Shielding: A User-Centric Approach Towards Trustworthy AI
Green Shielding introduces CUE criteria and the HCM-Dx benchmark to demonstrate that routine prompt variations systematically alter LLM diagnostic behavior along clinically relevant dimensions, producing Pareto-like tradeoffs in plausibility versus coverage.
-
S2H-DPO: Hardness-Aware Preference Optimization for Vision-Language Models
S2H-DPO generates hierarchical prompt-driven preference pairs to improve multi-image reasoning in VLMs while keeping single-image performance intact.
-
When Cow Urine Cures Constipation on YouTube: Limits of LLMs in Detecting Culture-specific Health Misinformation
LLMs fail to reliably detect culturally embedded health misinformation on YouTube because promotional and debunking content share similar rhetorical registers that blend tradition with pseudo-science, and this limitation persists across prompt variations.
-
Fine-Tuning Large Language Models for Cooperative Tactical Deconfliction of Small Unmanned Aerial Systems
Fine-tuning Qwen-Math-7B with LoRA and GRPO on BlueSky simulator data improves LLM accuracy and consistency in cooperative sUAS tactical deconfliction, reducing near mid-air collisions.
-
When Large Language Models Fail in Healthcare: Evaluating Sensitivity to Prompt Variations
Evaluation shows LLMs for healthcare are sensitive to prompt changes, leading to inconsistent and potentially harmful clinical outputs on MedMCQA.