Long Sequence Transformer Variants on Varying Context Length

Transformers are powerful and effective tools in natural language processing, but their scalability is limited by the quadratic complexity of attention. Several transformer variants that address this problem have recently been proposed, including Moving Average Equipped Gated Attention (Mega). In this thesis, we evaluate how effectively Mega uses past context, by comparing the perplexity trend as context length varies with the perplexity trend of a standard transformer. We find that Mega does not show greater benefit from longer context in a Wikipedia or book setting, though it does have a much better ability to extrapolate beyond training context lengths.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2023

Author dc:creator

• Sun, Melinda

Advisor dc:contributor.advisor

• Kim, Yoon

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/152839

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/152839