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LLM unbranding: Erasing Commercial Identity while Preserving Generic Utility
Kajetan Ożóg, Alicja Wojciechowska, Dawid Malarz +3
Establishing unbranding as a critical practice to prevent visual logos from acquiring negative connotations is standard in image generation. Large Language Models (LLMs) now face a…
CertMark: Distortion-Free Multi-Bit Watermarking with Certified Decoding
Paweł Batorski, Przemysław Spurek, Paul Swoboda
Leading multi-bit watermarking methods for language models encode messages by biasing the model's next-token probabilities, creating a trade-off between message recovery and text q…
MACRO: Markov Chain Routing of Transformer Layers
Paweł Batorski, Abtin Pourhadi, Akylgali Aitaza +2
Standard Large Language Models (LLMs) execute layers sequentially. Dynamic layer routing, i.e. search for a different execution path through layers involving layer repetitions, ski…
Spurious Prompts: Can Irrelevant Prompts Steer Large Language Models?
Pawel Batorski, Abtin Pourhadi, Jerzy Sarosiek +2
Large language models are highly sensitive to prompts, but this sensitivity is usually studied through task-relevant instructions, demonstrations, or reasoning cues. In this paper,…
TATRA: Training-Free Instance-Adaptive Prompting Through Rephrasing and Aggregation
Bartosz Dziuba, Kacper Kuchta, Paweł Batorski +2
Large Language Models (LLMs) have improved substantially alignment, yet their behavior remains highly sensitive to prompt phrasing. This brittleness has motivated automated prompt…
PIAST: Rapid Prompting with In-context Augmentation for Scarce Training data
Pawel Batorski, Paul Swoboda
LLMs are highly sensitive to prompt design, but handcrafting effective prompts is difficult and often requires intricate crafting of few-shot examples. We propose a fast automatic…
GPS: General Per-Sample Prompter
Pawel Batorski, Paul Swoboda
LLMs are sensitive to prompting, with task performance often hinging on subtle, sometimes imperceptible variations in phrasing. As a result, crafting effective prompts manually rem…