One Embedder, Any Task: Instruction-Finetuned Text Embeddings (2212.09741v3)

Abstract: We introduce INSTRUCTOR, a new method for computing text embeddings given task instructions: every text input is embedded together with instructions explaining the use case (e.g., task and domain descriptions). Unlike encoders from prior work that are more specialized, INSTRUCTOR is a single embedder that can generate text embeddings tailored to different downstream tasks and domains, without any further training. We first annotate instructions for 330 diverse tasks and train INSTRUCTOR on this multitask mixture with a contrastive loss. We evaluate INSTRUCTOR on 70 embedding evaluation tasks (66 of which are unseen during training), ranging from classification and information retrieval to semantic textual similarity and text generation evaluation. INSTRUCTOR, while having an order of magnitude fewer parameters than the previous best model, achieves state-of-the-art performance, with an average improvement of 3.4% compared to the previous best results on the 70 diverse datasets. Our analysis suggests that INSTRUCTOR is robust to changes in instructions, and that instruction finetuning mitigates the challenge of training a single model on diverse datasets. Our model, code, and data are available at https://instructor-embedding.github.io.

• The paper introduces InstructOR, which integrates task-specific instructions within text embeddings to create a unified multitask solution.
• It employs a contrastive learning approach on a large MEDI dataset with diverse tasks to enhance scalability and contextual relevance.
• Results reveal a 3.4% performance gain with fewer parameters, underscoring the efficiency and adaptability of instruction-finetuning.

Overview of "One Embedder, Any Task: Instruction-Finetuned Text Embeddings"

Introduction

The paper presents InstructOR, a novel approach to text embeddings which incorporates task-specific instructions directly within the embedding process. Unlike existing embedding models, InstructOR is designed to handle multiple downstream tasks without additional fine-tuning, generating embeddings that are tailored to specific tasks via a unified model. This paper proposes InstructOR as a robust, versatile solution to the challenges of task-specific and domain-specific text embeddings.

Methodology

InstructOR leverages a large, multitask dataset, MEDI, which consists of 330 datasets annotated with explicit instructional data. The framework utilizes a contrastive learning approach to train the model, enriching embeddings with contextual information supplied by task and domain-specific instructions. The architecture itself is based on the GTR encoder family, employing different model sizes to assess scalability and efficiency.

Evaluation and Results

A comprehensive evaluation was conducted on 70 different tasks, spanning various domains such as classification, semantic similarity, and retrieval. Impressively, InstructOR achieved a 3.4% improvement over prior models while utilizing significantly fewer parameters. The model's performance was notably robust across previously unseen tasks and domains, highlighting its broad applicability and efficiency.

Analysis and Implications

The paper offers a thorough analysis of the influences of model instruction, demonstrating that instruction-finetuning alleviates the burden of training embeddings on diverse datasets. The paper also emphasizes the resilience of InstructOR to instruction variations, facilitated by the task diversity within MEDI. Furthermore, results indicate that the model benefits from scaling up, suggesting the potential for further exploration with larger capacities.

Future Prospects

This research opens avenues for advancing universal text embeddings through instruction-based learning. Future studies could explore extending InstructOR to even larger models or integrating more sophisticated instructional elements, potentially involving demonstrations or explanation-based inputs.

Conclusion

In summary, the paper articulates a method for creating general-purpose text embeddings that effectively utilize task-specific instructions. InstructOR is positioned as a leading-edge solution in the field of text embeddings, demonstrating significant advancement in adaptability and performance across a spectrum of tasks and domains. The integration of instructional data highlights a promising direction for achieving a more nuanced and efficient multitask NLP model.