Leveraging Pre-trained Language Models to Build Multilingual and Cross-modal Retrieval Systems

Core Concepts

By extending the embedding layer of a pre-trained large language model (LLM) to support speech tokens, we can transform the LLM into an effective dual encoder retrieval system that can match speech and text in 102 languages, outperforming previous approaches that require significantly more speech-text training data.

Abstract

The authors propose a method to convert a pre-trained text-only large language model (LLM) into a dual encoder retrieval system that can match speech and text in multiple languages.
Key highlights:

They discretize speech into acoustic units using a pre-trained speech encoder and extend the embedding layer of the LLM to support these speech tokens, in addition to the text tokens.
They train the dual encoder model using a contrastive loss to align the speech and text embeddings.
Their model achieves a 10% absolute improvement in Recall@1 on speech-to-text retrieval across 102 languages, compared to a previous state-of-the-art model, despite being trained on only 21 languages.
The model also exhibits cross-lingual speech-to-text translation retrieval capabilities, which are further improved by incorporating readily available machine translation data.
The authors show that initializing speech models from pre-trained LLMs can effectively leverage the text-only multilingual capabilities of the LLMs.

Stats

Our model outperforms the mSLAM baseline by 10% absolute improvement in Recall@1 on speech-to-text retrieval across 102 languages.
Our model achieves a Recall@1 of 86.15% and a Word Error Rate of 13.85% on the 102 language FLEURS dataset for speech-to-text retrieval.

Quotes

"Our multi-modal LLM-based retrieval system is capable of matching speech and text in 102 languages despite only training on 21 languages."
"We achieve a 10% absolute improvement in Recall@1 averaged across these languages."
"Our model demonstrates cross-lingual speech and text matching, which is further enhanced by readily available machine translation data."

Key Insights Distilled From

Transforming LLMs into Cross-modal and Cross-lingual RetrievalSystems

by Frank Palma ... at arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.01616.pdf

Deeper Inquiries

How can the proposed approach be extended to other modalities beyond speech and text, such as images or video

To extend the proposed approach to other modalities beyond speech and text, such as images or video, a similar framework can be applied with some modifications. For images, the model can be trained on paired image-text data, where the images are encoded into embeddings using a pre-trained image encoder. These image embeddings can then be integrated into the existing model architecture alongside text embeddings. The model can be trained using a contrastive loss function to learn to align image and text representations in a shared embedding space. Similarly, for video data, frames can be treated as individual images, and the model can be trained on paired video-text data to learn cross-modal representations.

What are the potential limitations or challenges in scaling this approach to even larger language models or a broader set of languages

Scaling the approach to larger language models or a broader set of languages may face several potential limitations and challenges. One challenge is the computational resources required to train and fine-tune large language models on diverse datasets in multiple languages. Larger models may also face issues with overfitting and training instability, requiring careful regularization techniques and hyperparameter tuning. Additionally, scaling to a broader set of languages may introduce challenges related to data availability, quality, and linguistic diversity. Ensuring the model's generalization across a wide range of languages and modalities while maintaining performance can be a significant challenge.

How can the cross-lingual and cross-modal capabilities of the model be further improved, beyond the use of machine translation data

To further improve the cross-lingual and cross-modal capabilities of the model beyond the use of machine translation data, several strategies can be employed. One approach is to incorporate additional pre-training tasks that encourage the model to learn more robust cross-modal and cross-lingual representations. Tasks such as cross-modal retrieval, cross-lingual alignment, or multi-task learning with diverse datasets can help the model generalize better across languages and modalities. Additionally, leveraging self-supervised learning techniques, such as contrastive learning or generative modeling, can enhance the model's ability to capture semantic relationships and similarities across different modalities and languages. Regularly updating the model with new data and fine-tuning on specific cross-lingual and cross-modal tasks can also contribute to continuous improvement in performance.