Generative Representational Instruction Tuning
Authors: name: Niklas Muennighoff, name: Hongjin Su, name: Liang Wang, name: Nan Yang, name: Furu Wei, name: Tao Yu, name: Amanpreet Singh, name: Douwe Kiela

All text-based language problems can be reduced to either generation or
embedding. Current models only perform well at one or the other. We introduce
generative representational instruction tuning (GRIT) whereby a large language
model is trained to handle both generative and embedding tasks by
distinguishing between them through instructions. Compared to other open
models, our resulting GritLM 7B sets a new state of the art on the Massive Text
Embedding Benchmark (MTEB) and outperforms all models up to its size on a range
of generative tasks. By scaling up further, GritLM 8x7B outperforms all open
generative language models that we tried while still being among the best
embedding models. Notably, we find that GRIT matches training on only
generative or embedding data, thus we can unify both at no performance loss.
Among other benefits, the unification via GRIT speeds up Retrieval-Augmented
Generation (RAG) by > 60% for long documents, by no longer requiring separate
retrieval and generation models. Models, code, etc. are freely available at
https://github.com/ContextualAI/gritlm.