5 papers · 1 filter
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,…
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…
TATRA: Training-Free Instance-Adaptive Prompting Through Rephrasing and Aggregation
Bartosz Dziuba, Kacper Kuchta, PaweÅ 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…
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…