Repetition Improves Language Model Embeddings
Jacob Mitchell Springer Suhas Kotha Daniel Fried Graham Neubig Aditi Raghunathan
Recent approaches to improving the extraction of text embeddings from
autoregressive large language models (LLMs) have largely focused on
improvements to data, backbone pretrained language models, or improving
task-differentiation via instructions. In this work, we address an
architectural limitation of autoregressive models: token embeddings cannot
contain information from tokens that appear later in the input. To address this
limitation, we propose a simple approach, "echo embeddings," in which we repeat
the input twice in context and extract embeddings from the second occurrence.
We show that echo embeddings of early tokens can encode information about later
tokens, allowing us to maximally leverage high-quality LLMs for embeddings. On
the MTEB leaderboard, echo embeddings improve over classical embeddings by over
9% zero-shot and by around 0.7% when fine-tuned. Echo embeddings with a
Mistral-7B model achieve state-of-the-art compared to prior open source models
that do not leverage synthetic fine-tuning data.