Neural text embeddings are critical components of natural language processing (NLP) applications, acting as digital fingerprints for words and sentences. These embeddings are primarily generated by Masked Language Models (MLMs), but the advent of large Autoregressive Language Models (AR LMs) has prompted the development of optimized embedding techniques.
A key drawback to traditional AR LM-based embeddings is their left-to-right generation of text, causing the earliest words in a sentence to lack information from the following words. Meaning in text often depends on these subsequent words, meaning these embeddings may fail to capture the essence of a sentence. For instance, the word “summer” in “she loves summer for the warm evenings” and “she loves summer but dislikes the heat” would have the same embedding using traditional methods, despite their different connotations.
To address this limitation, researchers introduced “echo embeddings”, a technique where the input sentence is duplicated to compel the language model to consider the complete sentence. The practical application involves feeding the model a prompt to rewrite the sentence – “Rewrite the sentence: x, rewritten sentence: x”, and taking the embeddings from the duplicate words in the second part of the sentence. Consequently, the model accounts for the full context from earlier words, enhancing the quality of the embeddings.
An experiment was conducted to validate the effectiveness of echo embeddings. The test employed sentences with identical initial portions but contrasting latter parts that changed the overall meaning. Echo embeddings successfully differentiated these sentences, unlike traditional methods, hence capturing the information from the sentence’s latter position.
Echo embeddings also brought additional improvements, boosting performance across several NLP tasks by 9% in a zero-shot circumstance (without extra training). The technique maintained superior performance even after adjustment, as compared to classical embeddings. Nevertheless, a significant trade-off of echo embeddings is the doubling of the embedding creation cost, a vital consideration for real-time applications. Additionally, the cause behind the continued benefits of echo embeddings after fine-tuning remains obscure, as traditional embeddings appear to hit a representational dead-end.
In conclusion, echo embeddings present a novel method to augment the quality of embeddings generated from Autoregressive Language Models. This technique helps to broaden the application of powerful autoregressive language models for downstream NLP assignments by resolving a key shortfall. This could pave the way for more accurate search results, recommendations, and automatic text comprehension.
The complete research details can be found in the published article and on GitHub. All acknowledgements go to the researchers who carried out the project. More of their work and announcements can be followed on their social media platforms such as Twitter, Google News, a 38k+ ML SubReddit, 41k+ Facebook Community, Discord Channel, and LinkedIn Group. They also offer a newsletter and free AI courses for those interested.
