  Task embedding, a meta-learning technique that captures task-specific
information, has gained popularity, especially in areas such as multi-task
learning, model editing, and interpretability. However, it faces challenges
with the emergence of prompt-guided Large Language Models (LLMs) operating in a
gradient-free manner. Existing task embedding methods rely on fine-tuned,
task-specific language models, which hinders the adaptability of task
embeddings across diverse models, especially prompt-based LLMs. To hardness the
potential of task embeddings in the era of LLMs, we propose a framework for
unified task embeddings (FUTE), harmonizing task embeddings from various
models, including smaller language models and LLMs with varied prompts, within
a single vector space. Such uniformity enables comparison and analysis of
similarities amongst different models, broadening the scope and utility of
existing task embedding methods in multi-model scenarios, while maintaining
their performance comparable to architecture-specific methods.
