In the realm of natural language understanding, the ability to process long sequences of text such as stories, scientific articles, and lengthy documents is a significant challenge. This is due to the quadratic complexity of Transformer-based pretrained language models (LMs), which are widely used in this field. However, a recent study by Maor Ivgi and Uri Shaham proposes a simple yet effective solution to this problem: SLED (SLiding-Encoder and Decoder).
What is SLED?
SLED is a novel approach for processing long sequences of text that leverages existing short-text pretrained LMs. It works by partitioning the input into overlapping chunks, encoding each with a short-text LM encoder, and then using a pretrained decoder to fuse information across chunks. This method, also known as fusion-in-decoder, has been shown to be a viable strategy for long text understanding.
Processing a Long Report
Let's consider an example of processing a lengthy business report. The traditional approach would involve feeding the entire report into a language model, which would struggle due to the length of the document. With SLED, the report is divided into smaller, manageable chunks. Each chunk is then independently encoded using a short-text LM encoder. The encoded chunks are then fused together using a pretrained decoder, resulting in a comprehensive understanding of the entire report. This approach not only makes processing long reports feasible but also efficient and effective.
Processing a Research Paper
Similarly, when processing a lengthy research paper, SLED's approach proves to be highly beneficial. The research paper is divided into smaller chunks, each of which is independently encoded. The encoded chunks are then fused together using the pretrained decoder. This method allows for a thorough understanding of the research paper, including its complex ideas and findings, without being hindered by its length.
Advantages of SLED
SLED offers several advantages over traditional methods. Firstly, it is competitive with specialized models that are up to 50x larger and require a dedicated and expensive pretraining step. Secondly, it can be readily applied to any pretrained encoder-decoder LM, making it a versatile tool for organizations looking to integrate AI into their operations. Lastly, SLED's approach of processing long sequences in chunks makes it more efficient and manageable, overcoming the limitations of quadratic complexity in traditional LMs.
SLED is a promising approach for organizations and companies looking to integrate AI into their work. By partitioning long sequences into manageable chunks and using a fusion-in-decoder method, SLED offers an efficient and effective solution for understanding long text, making it a valuable tool for processing lengthy reports and research papers.
Ivgi, M. (2022). Efficient Long-Text Understanding with Short-Text Models. Retrieved from https://arxiv.org/abs/2208.00748
Ivgi, M., & Shaham, U. (2023). Efficient Long-Text Understanding with Short-Text Models. Transactions of the Association for Computational Linguistics, 11, 284–299. https://dx.doi.org/10.1162/tacl_a_00547