  Understanding the latent space of language models (LMs) is important for
improving the performance and interpretability of LMs. Existing analyses often
fail to provide insights that take advantage of the semantic properties of
language models and often overlook crucial aspects of language model
adaptation. In response, we introduce a pioneering approach called
vocabulary-defined semantics, which establishes a reference frame grounded in
LM vocabulary within the LM latent space. We propose a novel technique to
compute disentangled logits and gradients in latent space, not entangled ones
on vocabulary. Further, we perform semantical clustering on data
representations as a novel way of LM adaptation. Through extensive experiments
across diverse text understanding datasets, our approach outperforms
state-of-the-art methods of retrieval-augmented generation and
parameter-efficient finetuning, showcasing its effectiveness and efficiency.
