1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.CL2024★ 1 cited
Beyond Performance: Quantifying and Mitigating Label Bias in LLMs
Yuval Reif, Roy Schwartz
Large language models (LLMs) have shown remarkable adaptability to diverse tasks, by leveraging context prompts containing instructions, or minimal input-output examples. However,…
cs.CV2023
Segment (Almost) Nothing: Prompt-Agnostic Adversarial Attacks on Segmentation Models
Francesco Croce, Matthias Hein
General purpose segmentation models are able to generate (semantic) segmentation masks from a variety of prompts, including visual (points, boxed, etc.) and textual (object names)…