LaTeX Research Paper Templates

Which research paper template should I use?

How to write a research paper in LaTeX

1. 1. Pick the right template. Match the publisher's official class file. IEEE conferences expect IEEEtran. ACM venues expect acmart. Submitting in the wrong format wastes review time.
2. 2. Title and authors. Authors usually include affiliation and corresponding-author email. The exact format depends on the template — IEEE uses \IEEEauthorblockN, ACM uses \author with affiliation blocks.
3. 3. Abstract. 150-250 words. State the problem, your contribution, the headline result, and why it matters. Avoid jargon.
4. 4. Sections. Standard structure: Introduction → Related Work → Method → Experiments → Results → Discussion → Conclusion. Each section opens with a single-sentence thesis.
5. 5. Math, figures, tables. Wrap math in \[...\] for display equations. Use \begin{figure} and \begin{table} with captions and labels (e.g., \label{fig:method}) so you can reference them with \ref{fig:method}.
6. 6. References. Use BibTeX with a .bib file. Cite with \cite{key} and let LaTeX format the reference list automatically.
7. 7. Compile. Run pdflatex, then bibtex, then pdflatex twice to resolve references. The LaTeX to PDF tool handles the basic case in your browser.

Useful LaTeX snippets for research papers

Copy these into the relevant section of your paper.

\begin{figure}[t]
  \centering
  \includegraphics[width=0.9\linewidth]{architecture.pdf}
  \caption{System architecture overview.}
  \label{fig:arch}
\end{figure}

As shown in Fig.~\ref{fig:arch}, ...

\begin{table}[t]
  \centering
  \caption{Accuracy on WikiText-103.}
  \label{tab:results}
  \begin{tabular}{lcc}
    \toprule
    Method & PPL $\downarrow$ & Speedup $\uparrow$ \\
    \midrule
    Baseline & 18.4 & 1.0x \\
    Ours & 19.6 & 2.4x \\
    \bottomrule
  \end{tabular}
\end{table}

\usepackage{algorithm}
\usepackage{algorithmic}

\begin{algorithm}
\caption{Layer-wise Pruning}
\begin{algorithmic}[1]
  \STATE Input: model $M$, threshold $\tau$
  \FOR{layer $l$ in $M$}
    \STATE Compute importance $s_l$
    \IF{$s_l < \tau$}
      \STATE Prune $l$
    \ENDIF
  \ENDFOR
\end{algorithmic}
\end{algorithm}

\usepackage{amsthm}
\newtheorem{theorem}{Theorem}

\begin{theorem}[Convergence]
  If $\eta < 1/L$, gradient descent on a
  smooth convex $f$ converges at rate $O(1/k)$.
\end{theorem}

\begin{proof}
  Standard descent lemma yields ...
\end{proof}

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