Making Text Embedders Few-Shot Learners (2409.15700v1)

Abstract: LLMs with decoder-only architectures demonstrate remarkable in-context learning (ICL) capabilities. This feature enables them to effectively handle both familiar and novel tasks by utilizing examples provided within their input context. Recognizing the potential of this capability, we propose leveraging the ICL feature in LLMs to enhance the process of text embedding generation. To this end, we introduce a novel model bge-en-icl, which employs few-shot examples to produce high-quality text embeddings. Our approach integrates task-related examples directly into the query side, resulting in significant improvements across various tasks. Additionally, we have investigated how to effectively utilize LLMs as embedding models, including various attention mechanisms, pooling methods, etc. Our findings suggest that retaining the original framework often yields the best results, underscoring that simplicity is best. Experimental results on the MTEB and AIR-Bench benchmarks demonstrate that our approach sets new state-of-the-art (SOTA) performance. Our model, code and dataset are freely available at https://github.com/FlagOpen/FlagEmbedding .

• The paper introduces bge-en-icl, integrating in-context learning into text embeddings to achieve SOTA performance.
• It embeds few-shot examples into prompts to adapt dynamically to diverse NLP tasks without altering the original architecture.
• Experimental results on MTEB and AIR-Bench show robust improvements, including a score of 71.67 in few-shot settings.

Making Text Embedders Few-Shot Learners

The paper, "Making Text Embedders Few-Shot Learners," introduces a novel model, bge-en-icl, with the primary objective of enhancing text embeddings through in-context learning (ICL) capabilities derived from LLMs. The research focuses on seamlessly integrating ICL and text embedding to achieve state-of-the-art (SOTA) performance across multiple benchmarks.

Overview and Background

Text embeddings are vector representations of natural language text, pivotal in various NLP tasks such as information retrieval, text classification, item recommendation, and question answering. While pre-trained bidirectional encoder and encoder-decoder architectures have been extensively used for generating high-quality text embeddings, recent advancements have shifted towards embedding models based on decoder-only LLM architectures. These models have demonstrated impressive in-domain accuracy and generalization, especially when trained using supervised learning approaches. However, embedding models still face challenges when dealing with unseen task instructions and complex retrieval tasks. This limitation is primarily due to the narrow range of instructions encountered during training compared to the broader variety of real-world embedding tasks.

In-Context Learning (ICL) Strategy

ICL leverages the ability of LLMs to incorporate task-specific examples directly into input prompts to generate the desired outputs. This extends beyond tasks seen during training, enabling LLMs to dynamically adapt to novel tasks without additional training. The study capitalizes on the ICL capability of LLMs to improve text embeddings' adaptability, intending to enhance the generalization and relevance of embeddings across diverse contexts and tasks. Specifically, the approach involves embedding few-shot examples into the query side of prompts, which guides the model to produce high-quality text embeddings tailored to the specific task at hand.

Model Architecture and Experimental Setup

The authors advocate for retaining the original model architecture, emphasizing simplicity. Experimental findings suggest that complex modifications to the model do not lead to significant performance improvements. Therefore, integrating ICL capabilities into the text embedding process without modifying the underlying architecture is the primary methodological innovation.

The model was tested on two prominent benchmarks: MTEB and AIR-Bench, demonstrating improved performance and achieving SOTA results. The experiments involved various tasks, including retrieval, reranking, clustering, pair classification, classification, STS, and summarization.

Evaluation and Main Results

On the MTEB benchmark, bge-en-icl achieved an overall performance score of 71.67 in few-shot settings, surpassing previous models by a considerable margin. Moreover, the research extends to bilingual and multilingual contexts, exploring the efficacy of the ICL-based model across different languages and datasets such as C-MTEB, FR-MTEB, and MIRACL.

Implications and Future Directions

The paper's findings highlight the potential of LLMs' ICL capabilities to revolutionize the text embedding landscape. The ability to dynamically adapt to novel tasks without additional fine-tuning or architectural modifications signifies a substantial leap in the versatility and generalization of embedding models. Moreover, the approach opens new avenues for enhancing multilingual and domain-specific embeddings, providing robust performance across diverse tasks and languages.

Future research could explore exploring the ICL capabilities in more complex multilingual settings and extending the approach to other types of LLMs. Additionally, investigating the optimization of embedding generation techniques in specific domains, such as legal or medical texts, could further enhance model performance and applicability.

Conclusion

The paper presents a compelling case for leveraging ICL capabilities in LLMs to generate high-quality text embeddings. The simplicity of maintaining the original architecture while embedding ICL capabilities underscores the effectiveness of the approach, leading to SOTA results on major benchmarks. This study marks a significant step towards more adaptable, versatile, and efficient text embedding models, setting a solid foundation for future research and development in NLP.