CMU researchers present 'Echo Embeddings': an embedding strategy designed to address an architectural limitation of autoregressive models

Neural text embeddings play a critical role in many modern natural language processing (NLP) applications. These embeddings are like fingerprints of words and sentences that enable tasks such as judging similarities or finding related documents. Traditionally, masked language models (MLM) have dominated the generation of these embeddings. However, recent advances in large autoregressive language models (AR LM) have sparked interest in developing integration techniques optimized for this type of model.

A major flaw of traditional AR LM embeddings is an inherent limitation: AR LMs generate text from left to right, causing embeddings of the first few words in a sentence to lose information from later words. This can be a problem because the meaning can often depend on those later words. Consider the phrases “He loves summer for the warm nights” and “He loves summer but doesn't like the heat.” The word “summer” would have the same inclusion in both sentences if traditional techniques were used, missing a key distinction provided by the latter parts of the sentences.

Researchers have introduced a surprisingly simple strategy called “echo embeddings” to address this problem. The core idea is to repeat the input sentence twice, which effectively forces the language model to pay attention to the entire sentence. Let's illustrate how this works with an example:

• Classic inlays: Feed the phrase x to the language model and take the embeddings of each word.
• Echo additions: Enter the message “Rewrite the sentence: xrewritten sentence: x to the language model. Now, take the embeddings of the second occurrence of those same words.

By focusing on the second occurrence of words, the echo embedding strategy ensures that the model embeds the full meaning of the sentence. This subtle change has a powerful impact on the quality of the resulting inlays.

To demonstrate that echo inlays In their work, the researchers designed a clever experiment. The experiment used sentences in which the first parts were identical, but the last parts were different in a way that altered the meaning. Echo embeddings were able to distinguish between sentences, while classical embeddings were not. This suggests that the echo strategy allows early word embeddings to capture information from later words in the sentence.

Researchers also found that echo inlays offer additional benefits. In a zero-shot setting (no additional training), echo embeddings improved performance by 9% across a broad benchmark of NLP tasks. Even after fine-tuning, the Echo inlays still outperformed the classic inlays.

While echo keying is a promising technique, there are trade-offs. They double the cost of creating the embedding, which can be important for real-time applications. Furthermore, it is not fully understood why echo embeddings continue to provide benefits even after fine-tuning, while traditional embeddings appear to have a representational bottleneck.

In conclusion, echo embeddings are an innovative technique to improve the quality of embeddings generated from autoregressive language models. This work helps open the door to broader use of powerful autoregressive language models in downstream NLP tasks by overcoming a key limitation, which could lead to even better search results, recommendations, and automated text understanding.

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