Generative Representational Instruction Tuning: A Unified Model for Text Embedding and Generation

Core Concepts

A single large language model can be trained to handle both text embedding and generation tasks by distinguishing between them through instructions, outperforming specialized models on both tasks.

Abstract

The content introduces Generative Representational Instruction Tuning (GRIT), a method to unify text embedding and generation tasks within a single large language model.
Key highlights:

Current models perform well at either embedding or generation, but not both. GRIT aims to create a single model that excels at both.
GRIT combines two training paradigms: generative instruction tuning (generating an answer based on an instruction) and representational instruction tuning (representing an input according to an instruction).
GRITLM 7B sets a new state-of-the-art on the Massive Text Embedding Benchmark (MTEB) while outperforming larger models on generative tasks.
GRITLM 8X7B is the best open generative language model on the task average, while only using 13B parameters at inference.
Unifying embedding and generation in a single model simplifies infrastructure, speeds up Retrieval-Augmented Generation, and matches the performance of specialized models.
Experiments explore the impact of attention, pooling, datasets, batch size, precision, and loss functions on the unified model's performance.

Stats

GRITLM 7B sets a new state-of-the-art on the Massive Text Embedding Benchmark (MTEB) among open models.
GRITLM 7B outperforms much larger models like Llama 2 70B on generative tasks.
GRITLM 8X7B is the best open generative language model on the task average, while only using 13B parameters at inference.
Reranking the top 10 documents using GRITLM 7B's generative capabilities improves the embedding performance on retrieval datasets.

Quotes

"GRIT unifies representational instruction tuning and generative instruction tuning into a single model."
"Our unified model matches the performance of embedding-only and generative-only variants, even outperforming them on some tasks."
"Generative and embedding models are commonly used together to make up for each other's deficiencies. With GRITLM, the embedding and generative model are equivalent, allowing us to cache computations and halve the necessary number of forward passes."

Key Insights Distilled From

Generative Representational Instruction Tuning

by Niklas Muenn... at arxiv.org 04-18-2024

https://arxiv.org/pdf/2402.09906.pdf

Deeper Inquiries

How can the GRIT approach be extended to other modalities beyond text, such as images or multimodal tasks?

The GRIT approach can be extended to other modalities beyond text by adapting the training process to incorporate multiple modalities. For images, the model can be trained to handle both image embedding tasks and image generation tasks by providing instructions that distinguish between the two. The model can be finetuned with data that includes both image representation and generation examples in a consistent format. By using a unified model like GRIT, the model can learn to excel at both tasks simultaneously. For multimodal tasks, the model can be trained on data that includes instructions for processing different modalities together, such as text and images. This way, the model can learn to generate responses that incorporate information from multiple modalities.

What are the potential drawbacks or limitations of a single unified model compared to specialized models for certain tasks?

While a single unified model like GRIT offers the advantage of handling multiple tasks efficiently, there are potential drawbacks and limitations to consider. One limitation is the increased compute required during training due to the model having to optimize for multiple objectives simultaneously. This can lead to longer training times and higher computational costs compared to training separate specialized models for specific tasks. Additionally, a single unified model may not perform as well as specialized models that are optimized for a specific task, especially in cases where task-specific architectures or optimizations are necessary. There may also be challenges in fine-tuning the model for different tasks without sacrificing performance on either task.

How might the GRIT training process be further optimized to reduce the additional compute required compared to training separate embedding and generative models?

To reduce the additional compute required for training a unified model like GRIT compared to training separate embedding and generative models, several optimization strategies can be implemented. One approach is to explore more efficient training techniques, such as distributed training or mixed precision training, to speed up the training process and reduce computational costs. Additionally, optimizing the model architecture for faster convergence and better parameter efficiency can help reduce the overall compute required. Another strategy is to carefully design the training data and loss functions to focus on the most important aspects of each task, thereby reducing unnecessary computations. Furthermore, leveraging pretraining and transfer learning techniques can help initialize the model with knowledge from related tasks, reducing the amount of compute needed during fine-tuning. By implementing these optimization strategies, the GRIT training process can be made more efficient and cost-effective.