Echo embeddings offer a novel solution to enhance autoregressive language models by incorporating information from later tokens in the input sequence. This is achieved by repeating the input, allowing the model to capture context from subsequent tokens. The approach demonstrates strong performance on the MTEB benchmark and can be integrated with existing embedding improvement techniques.
For practical application, users can easily access a pretrained model on HuggingFace, and the provided code snippets facilitate the use of echo embeddings. The process involves importing necessary modules, defining templates for embedding, and setting up the model, parser, and pooling strategy. Users can choose between mean or last token pooling strategies, and the model supports symmetric similarity computations for sentence-level comparisons.
The method is straightforward to implement, with the ability to parse inputs, run the model, and pool the embeddings to extract sentence representations. These representations can then be used to calculate cosine similarity between different text inputs, such as queries and documents.