Efficient Retrieval-Augmented Language Models with Binary Token Representations

Основные понятия

Binary token representations can significantly improve the inference speed and reduce the storage footprint of retrieval-augmented language models while maintaining high task performance.

Аннотация

The paper introduces Binary Token Representations (BTR), a technique to improve the efficiency of retrieval-augmented language models. Retrieval-augmented language models use a retrieve-and-read pipeline, where a retriever finds relevant passages and a reader model generates the output. The reader model is the computational bottleneck, as it needs to process a large number of retrieved passages.

BTR addresses this by precomputing binary token representations for the retrieved passages. The key ideas are:

1. Calibrated binarization: BTR binarizes the token representations in the reader encoder layers, using a calibration technique to preserve the representation quality.
2. Offline compression: BTR further compresses the binary token representations by merging similar representations, reducing the storage footprint.
3. Training objectives: BTR introduces two training objectives - passage representation recovery and query-aware passage token distillation - to mitigate the accuracy loss from binarization and decomposition.
4. Runtime compression: BTR applies additional compression techniques during inference to further improve the speed of the reader encoder and decoder.

The experiments show that BTR can accelerate state-of-the-art retrieval-augmented language models by 2-4x, reduce storage by over 100x, while maintaining over 95% of the original models' performance on five knowledge-intensive NLP tasks.

Статистика

The paper reports the following key metrics:

BTR-Atlas base model achieves 49.5% EM on NaturalQuestions, 66.7% EM on TriviaQA, 43.8% EM on WebQuestions, 70.2% accuracy on FEVER, and 35.4% accuracy on MMLU.
BTR-Atlas base model achieves 3.1x speedup over the Atlas base model on inference throughput.
BTR-Atlas large model achieves 56.1% EM on NaturalQuestions, 70.8% EM on TriviaQA, 49.1% EM on WebQuestions, 75.9% accuracy on FEVER, and 39.2% accuracy on MMLU.
BTR-Atlas large model achieves 4.0x speedup over the Atlas large model on inference throughput.
The storage footprint of BTR-Atlas base is 127GB, compared to 12,804GB for the baseline DeFormer model.

Цитаты

"BTR reduces the storage footprint and improves the runtime speed since the representations are 1-bit vectors, and the reader uses the cached representations."
"BTR tackles the challenge by building compact binary token representations for the passages."
"BTR presents better efficiency versus accuracy trade-offs by maintaining high accuracy and inference throughput with a smaller storage footprint."

Ключевые выводы из

BTR: Binary Token Representations for Efficient Retrieval Augmented Language Models

by Qingqing Cao... в arxiv.org 05-06-2024

https://arxiv.org/pdf/2310.01329.pdf

Дополнительные вопросы

How can BTR be extended to work with decoder-only reader models, which compute passage representations together with the query in a sequential manner?

To extend BTR to work with decoder-only reader models, we need to address the challenge of caching passage representations in a sequential computation setting. Unlike encoder models, where passage representations can be precomputed and stored, decoder models compute passage representations along with the query in a sequential manner. One approach to tackle this challenge is to leverage techniques like key-value caches in the decoder to speed up inference decoding. By storing binary representations of passages in a key-value store, we can efficiently retrieve and use them during the sequential computation process. Additionally, optimizing the binary representation retrieval process in the decoder can help maintain efficiency while ensuring accurate decoding of passages in the context of the query.

How can BTR be scaled to work with larger language models with bigger representation sizes, potentially using techniques like autoencoders for compression?

Scaling BTR to work with larger language models with bigger representation sizes involves optimizing the binary token representations to handle the increased complexity and size of the models. One potential approach is to leverage techniques like autoencoders for compression. Autoencoders can help reduce the dimensionality of the representations while preserving essential information, making them more manageable for storage and retrieval. By incorporating autoencoders into the BTR framework, we can effectively compress the representations of larger language models without compromising the quality of the information encoded in the tokens. This compression technique can enable BTR to scale efficiently to handle the demands of larger models.

Can BTR be incorporated into the pretraining process of retrieval-augmented language models to build more efficient models from the ground up?

Incorporating BTR into the pretraining process of retrieval-augmented language models can indeed help build more efficient models from the ground up. By integrating binary token representations into the architecture during pretraining, the models can benefit from the efficiency gains offered by BTR from the initial stages of training. This integration can involve optimizing the training objectives to include the calibration of binary representations, training regularization techniques for accuracy preservation, and token compression methods for enhanced efficiency. By incorporating BTR from the pretraining phase, the models can be designed to be inherently efficient in handling retrieval and inference tasks, leading to improved performance and reduced storage requirements throughout the training and deployment phases.