Representation Editing

Representation editing focuses on modifying the internal representations of large language models (LLMs) to improve their performance and alignment with human objectives, offering a parameter-efficient alternative to full model fine-tuning. Current research explores methods like low-rank linear subspace interventions and scaling/biasing operations applied to hidden layers, aiming to mitigate issues such as hallucinations and improve control over model outputs. These techniques offer significant advantages in terms of computational efficiency and resource requirements, impacting both the development of more robust LLMs and the accessibility of advanced language technologies.