Loading Now

Summary of Longssm: on the Length Extension Of State-space Models in Language Modelling, by Shida Wang


LongSSM: On the Length Extension of State-space Models in Language Modelling

by Shida Wang

First submitted to arxiv on: 4 Jun 2024

Categories

  • Main: Computation and Language (cs.CL)
  • Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Dynamical Systems (math.DS)

     Abstract of paper      PDF of paper


GrooveSquid.com Paper Summaries

GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!

Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
This paper explores the length-extension capabilities of state-space models (SSMs) in language modeling, focusing on the challenges and solutions when training models on short sequences and testing them on longer ones. The authors demonstrate that SSMs initialized with zero hidden states struggle with length extension, which is equivalent to polynomial extrapolation. To address this limitation, they propose a simple yet effective method: changing the hidden state initialization scheme. This approach enables efficient training of long-memory models using smaller training context lengths.
Low GrooveSquid.com (original content) Low Difficulty Summary
This paper looks at how well state-space models can handle longer texts in language modeling. Right now, these models are good at understanding short texts but struggle with longer ones. The researchers figure out why this is happening and then come up with a simple solution to make the models better. They show that using shorter training sequences doesn’t always mean you’ll get worse results. By changing how they start their calculations, they can train models that understand long texts without needing super-long training.

Keywords

» Artificial intelligence