108 lines
6.7 KiB
Markdown
108 lines
6.7 KiB
Markdown
# Writing guide: section by section
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Read the section you are about to write, then write it into `paper/sections/<name>.tex`.
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Every section is plain LaTeX without `\section` preamble tricks; cite with `\citep{key}` /
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`\citet{key}` (natbib), reference with `\cref{fig:x}` / `\cref{tab:x}` / `\cref{sec:x}`.
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General style
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- One claim per sentence; every claim is backed by (a) a table/figure of this paper,
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(b) a citation, or (c) a derivation. If none exists, delete the claim.
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- Use the method name macro `\ours{}` consistently. Define every symbol once, at first use.
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- Present tense for what the paper does ("we propose", "Table 2 shows"), past tense for
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what was run ("we trained for 100 epochs").
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- No hype words: novel, groundbreaking, remarkable, significantly (unless a test was run),
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state-of-the-art (unless compared against the current best published number on the
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same benchmark with the same protocol, cited).
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- Numbers: same number of decimals as the table; units; "x pp" for percentage-point
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differences. Quote only values from `paper/data/summary.csv`, the generated tables, or a
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cited paper (then cite it in the same sentence).
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- Paragraphs open with a topic sentence; a reader who reads only first sentences gets
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the story.
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## abstract.tex (150-250 words, no citations, no math)
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1. Context + problem (1-2 sentences). 2. Gap: why existing methods fall short (1).
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3. What we do: method in one sentence, the key idea (1-2). 4. Evidence: datasets and the
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headline result with a number (1-2). 5. Implication / what is released (1).
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## introduction.tex (~1 page) - `\section{Introduction}\label{sec:intro}`
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- P1 Motivation: the task and why it matters, with 2-4 citations.
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- P2 The gap: what current approaches do and the specific limitation, cited.
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- P3 Our idea: the insight in plain words; optionally point to the teaser figure.
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- P4 Evidence: what was evaluated and the headline numbers.
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- Contributions as an `itemize` of 3-4 bullets, each concrete and checkable
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("We show on X and Y that ... improves Z by N pp over B", not "extensive experiments").
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- Optional last line: paper outline (skip if tight on space).
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## related_work.tex (0.5-1 page) - `\section{Related Work}\label{sec:related}`
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- 2-4 `\paragraph{Theme.}` blocks, grouped by idea, not a list of papers.
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- Each paragraph: what the line of work does (cite 3-8 papers), then **how this paper
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differs** in one sentence. Never misrepresent a paper: describe only what its
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abstract/text (as summarised in research.md) says.
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- Cite the original paper of every baseline, dataset, model and major library used.
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## method.tex (1.5-2.5 pages) - `\section{Method}\label{sec:method}`
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- Problem setting / notation first (`\subsection{Problem Setting}`): inputs, outputs,
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objective, as equations.
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- Then the method, in the order the code executes it. Every equation must match the code
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(loss terms, normalisations, hyperparameters as symbols). Read the code again while
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writing this; if the code differs from the "intended" method, describe the code.
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- An algorithm box (`algorithm` + `algorithmic`) if there is a training/inference loop.
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- Complexity / cost if relevant (params, FLOPs, wall-clock - only if measured).
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- Design choices: say why, ideally pointing to an ablation.
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## experimental_setup.tex (~0.75 page) - `\section{Experimental Setup}\label{sec:setup}`
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Reproducibility details taken from the code/configs, not invented:
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- Datasets (cite each): size, splits, preprocessing, augmentation.
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- Baselines (cite each) and how they were tuned (same budget?).
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- Architecture, optimizer, learning rate + schedule, batch size, epochs/steps, weight
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decay, early stopping, seeds (number of runs), hardware and run time if logged.
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- Metrics, with direction (higher/lower is better) and how they are aggregated
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(mean +- std over N seeds; test-set at last epoch or at best validation epoch).
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- Put long hyperparameter tables in the appendix and reference them.
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## results.tex (1.5-2.5 pages) - `\section{Results}\label{sec:results}`
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- `\subsection{Main Results}`: `\input{tables/main}` + 1 paragraph per finding. Each
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paragraph: observation -> numbers -> interpretation. Mention where the method does
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NOT win.
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- `\subsection{Ablations}` (if variant/ablation runs exist): what each component
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contributes, `\input{tables/ablation}` or a bar figure.
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- `\subsection{Analysis}`: learning curves, sensitivity to a hyperparameter, efficiency
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trade-off, qualitative examples - whatever the data supports.
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- Figures: `\begin{figure}[t]\centering\includegraphics[width=\linewidth]{figures/x.pdf}
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\caption{What is shown. What to notice. Shaded: +-1 std over N seeds.}\label{fig:x}\end{figure}`.
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Captions are self-contained; every figure/table is referenced in the text before it appears.
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- Significance: only call a difference significant if a test was run (the dagger marks
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in results.py tables); otherwise say "higher on average" and give std.
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## discussion.tex (~0.5 page) - `\section{Discussion and Limitations}\label{sec:discussion}`
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- What the results mean, when to use the method, when not.
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- Limitations honestly and specifically: few seeds, small datasets, single domain, no
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hyperparameter search for baselines, compute limits, missing comparisons. Reviewers
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trust a paper more when it names its weaknesses.
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## conclusion.tex (1 paragraph) - `\section{Conclusion}\label{sec:conclusion}`
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Restate the problem, the idea, the evidence; one sentence of future work. No new claims.
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## statements.tex
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`\section*{Reproducibility Statement}`: where code/configs/seeds are, which sections/appendix
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give the details. `\section*{Broader Impact}` (or Ethics Statement): 3-6 honest sentences;
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dataset licences if relevant. Keep what the target venue requires, drop the rest.
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## appendix.tex
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`\section{Additional Experimental Details}\label{app:details}` (full hyperparameter table,
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compute), `\section{Additional Results}` (per-dataset tables, more curves). Every appendix
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section is referenced from the main text.
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## Title
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Specific and searchable: "<Method>: <what it does> for <task>" or a finding-style title
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("<X> improves <Y> under <condition>"). No question titles, no puns unless the user wants them.
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## Self-review checklist (do it as a reviewer, write findings to paper/review.md)
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- Is the contribution clear after page 1? Are claims in abstract/intro all supported by results?
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- Are baselines fair (same data, budget, tuning)? Are the strongest relevant baselines there?
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- Seeds/variance reported? Test set used only for final numbers?
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- Are related works that do nearly the same thing cited and differentiated?
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- Do equations match the code? Notation consistent? All symbols defined?
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- Every figure readable in grayscale print, axes labelled with units, fonts >= 7pt?
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- Limitations stated? Anything over-claimed ("significantly", "state of the art")?
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