Repetition Improves Language Model Embeddings (2402.15449v1)

Abstract: Recent approaches to improving the extraction of text embeddings from autoregressive LLMs have largely focused on improvements to data, backbone pretrained LLMs, 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.

• The paper presents a novel echo technique that improves autoregressive LLM embeddings by integrating future token context.
• It repeats input sentences to enable bidirectional information capture, yielding over 9% performance improvements on the MTEB benchmark.
• The method offers a practical solution with theoretical insights, paving the way for future enhancements in embedding optimization.

Enhancing Autoregressive LLM Embeddings with Echo Technique

Introduction to Echo Embeddings

The objective of improving text embeddings has been a central theme in the deployment of neural networks to tasks like information retrieval, semantic similarity estimation, classification, and clustering. A paper presents a novel approach to generating embeddings from autoregressive LLMs for these purposes, addressing a core limitation: their inability to incorporate information from subsequent tokens in generating embeddings for a given token. This work introduces the concept of "echo embeddings," which effectively incorporates future context into embeddings by repeating the input sentence within the model's context. The paper reports a significant performance improvement on the Massive Text Embedding Benchmark (MTEB), establishing echo embeddings as a potent method for leveraging the strengths of autoregressive LLMs in generating text embeddings.

Methodology and Findings

Dealing with Autoregressive Models' Limitation

The paper identifies a notable limitation with autoregressive LLMs in that they cannot utilize future token information in the generation of embeddings, which can lead to suboptimal performance in applications requiring a holistic understanding of the text. To counter this, the authors propose echo embeddings, which involve repeating the input sentence, allowing the model to attend to the entire input when encoding each token during the second occurrence. This method enables early tokens to encompass information from later portions of the text, overcoming the inherent limitation of autoregressive models.

Empirical Validation

Through extensive experiments, the authors demonstrate the efficacy of echo embeddings. The performance of echo embeddings was benchmarked using the MTEB, showcasing an improvement of over 9% in a zero-shot setting and consistent gains across various tasks when compared to classical embeddings. Additionally, the experimentation on synthetic data further affirmed that echo embeddings could capture bidirectional information, enabling it to outperform classical embeddings in scenarios where early tokens only superficially suggested similarity.

Practical and Theoretical Implications

The implementation of echo embeddings represents an easily adaptable method that could be integrated with existing or future enhancements in autoregressive LLM embeddings. Theoretical implications of this research suggest a pathway for maximizing the informational content of embeddings derived from autoregressive models, potentially paving the way for more sophisticated and contextually aware neural network architectures.

Future Outlook

Looking forward, the concept introduces promising avenues for further exploration. While the immediate benefits to information retrieval and related applications are clear, understanding the deeper mechanics of why echo embeddings yield performance gains, especially after fine-tuning, warrants additional research. The method's computational efficiency, mainly since it necessitates processing inputs twice, might be an area ripe for optimization.

Additionally, the conceptual framework of echo embeddings could inspire comparable methodologies across different model architectures, not limited to text data. As autoregressive models continue to evolve, integrating echo embeddings or similar approaches could become a standard practice for generating high-quality embeddings, contributing further to advancements in machine learning and AI.

Conclusion

The introduction of echo embeddings marks a significant development in the field of neural text embeddings, particularly for autoregressive LLMs. By ingeniously addressing a critical limitation of these models, the researchers have not only demonstrated substantial performance improvements but also opened new horizons for future research and applications. As the AI community continues to strive for more contextually rich and informative embeddings, techniques like echo embeddings will likely play a crucial role.