Don't Reinvent the Wheel: Efficient Instruction-Following Text Embedding based on Guided Space Transformation (2505.24754v1)

Abstract: In this work, we investigate an important task named instruction-following text embedding, which generates dynamic text embeddings that adapt to user instructions, highlighting specific attributes of text. Despite recent advancements, existing approaches suffer from significant computational overhead, as they require re-encoding the entire corpus for each new instruction. To address this challenge, we propose GSTransform, a novel instruction-following text embedding framework based on Guided Space Transformation. Our key observation is that instruction-relevant information is inherently encoded in generic embeddings but remains underutilized. Instead of repeatedly encoding the corpus for each instruction, GSTransform is a lightweight transformation mechanism that adapts pre-computed embeddings in real time to align with user instructions, guided by a small amount of text data with instruction-focused label annotation. We conduct extensive experiments on three instruction-awareness downstream tasks across nine real-world datasets, demonstrating that GSTransform improves instruction-following text embedding quality over state-of-the-art methods while achieving dramatic speedups of 6~300x in real-time processing on large-scale datasets. The source code is available at https://github.com/YingchaojieFeng/GSTransform.

Overview of an Efficient Instruction-Following Text Embedding Framework

The paper "Don't Reinvent the Wheel: Efficient Instruction-Following Text Embedding based on Guided Space Transformation" by Feng et al. introduces GSTransform, a framework aimed at enhancing the functionality of instruction-following text embeddings while markedly improving computational efficiency. This is achieved through Guided Space Transformation, which aligns pre-computed embeddings with user-specific instructions without needing to re-encode the entire corpus per instruction. The GSTransform framework incorporates two primary components: instruction-based label construction and label-guided embedding transformation, facilitating dynamic adaptation to user instructions while preserving the overall structure of the original embedding space.

Key Contributions

1. Reduction of Computational Overhead: GSTransform effectively addresses the drawback of existing methods that necessitate re-encoding large datasets for each instruction. By transforming pre-computed embeddings through a lightweight mechanism, GSTransform demonstrates a substantial improvement in processing speed, achieving real-time processing speedups of 6 to 300 times compared to state-of-the-art methods.
2. Instruction-based Label Construction: Utilizing a sampled subset of texts, GSTransform generates labels that align with user instructions to guide embedding transformation. This approach circumvents exhaustive labeling efforts and relies on cluster analysis of instruction-guided summaries.
3. Label-guided Embedding Transformation: The framework employs a simple encoder-decoder architecture, leveraging instruction-driven labels to transform embeddings according to user instructions. This allows the model to emphasize instruction-specific information without regenerating embeddings from scratch.
4. Robust Empirical Validation: Through extensive experiments on nine datasets spanning tasks such as clustering, semantic textual similarity, and triplet alignment, GSTransform consistently enhances embedding quality. Notably, GSTransform exhibits versatility across various generic embedding models and maintains robust performance improvements even with changes in hyperparameters, such as sample size and clustering granularity.

Implications and Future Directions

This research provides a pragmatic approach for text embedding systems in real-world applications requiring rapid response conditions, such as interactive search engines, sentiment analysis platforms, and personalized recommendation systems. The framework's ability to adapt to nuanced user instructions without extensive computational costs positions it as a valuable tool for scalable AI systems.

The findings open avenues for future work to explore more sophisticated architectures within the transformation model, potentially incorporating non-linear layers or attention-based mechanisms for enhanced fidelity in representation. There is also scope for integrating coreset selection techniques to ensure representative and balanced sampling in data outsourcing tasks.

In summary, GSTransform establishes a new standard for efficient, instruction-following text embedding by demonstrating how existing embeddings can be dynamically transformed into instruction-specific semantic spaces, thus optimizing both computational resources and user-centric functionality in text-based AI applications.