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Thesis & Dissertation

Thesis front matter in LaTeX: abstract and acknowledgments

The pages between the title page and chapter one: the abstract, acknowledgments, and dedication, each on its own page with the conventional headings.

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Page 1 of the Front Matter template, compiled with pdfTeX
Page 1, compiled with pdfTeX. This is the actual output.

Who it's for

Use it alongside the title page and chapter templates. These pages are often written last and checked most carefully.

How to customize this template

  1. Keep the abstract within your school's word limit (often 350 words for dissertations).
  2. In acknowledgments, thank your advisor and committee first, then labmates, funding sources, and family.
  3. Delete the dedication page if you don't want one. It's optional almost everywhere.
  4. Check that front-matter pages use roman numerals if your school requires it.

When it looks right, press Download PDF. To take the .tex to Overleaf or a local TeX install, choose Export → LaTeX source.

The first lines of the source

\documentclass{report}

\begin{document}

\begin{center}
{\Large \textbf{Efficient Attention Mechanisms for Long-Context Transformer Models}} \\
Your Name
\end{center}

\bigskip
\section*{Abstract}
This dissertation studies sparse and learned-routing attention mechanisms for transformer models operating on long sequences. We show that routing quality, rather than sparsity ratio alone, governs the trade-off between computational efficiency and model accuracy, and we characterize how this trade-off shifts with model scale. Across model sizes from 125M to 2.7B parameters, we find that larger models tolerate more aggressive sparsification, narrowing the gap to dense attention as scale increases. Our findings inform the design of compute-optimal long-context architectures and motivate treating sparsity ratio as a scale-dependent hyperparameter rather than a fixed architectural choice.

\section*{Acknowledgments}
I am deeply grateful to my advisor, Mara Okafor, for her patient guidance and for the countless whiteboard sessions that shaped this work. Thanks also to my committee members, Elena Vasquez and David Torres, for their careful feedback at every stage. I thank my labmates for years of shared debugging sessions and honest criticism, and the Fairview systems team for keeping the cluster running through more deadline-week jobs than I'd like to admit.

Finally, thanks to my family for their unwavering support throughout this long process.

…

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