inference-time-adaptation

MIITA is a memory-induced inference-time adaptation framework for continual learning with small language models. It stores correction-direction prototypes and applies gated hidden-state adaptation at inference time to mitigate catastrophic forgetting without updating backbone parameters.

This paper presents the DFKI-MLT system for SemEval-2026 Task 7 on cultural awareness, which applies activation steering to multilingual LLMs using language vectors from parallel FLORES data. The system achieved 86.96% accuracy in the MCQ track, ranking 7th out of 17 teams, and post-hoc analyses reveal that gains are layer-sensitive and vary across language-region pairs.