| name | acl-writing-style |
| description | Use when revising an ACL paper for computational-linguistics house style, covering task-first framing, linguistic examples tied to quantitative error analysis, scoping language claims to tested languages, LLM-era claim discipline, anonymous self-reference, Limitations prose, and compressing into the 8-page or 4-page ACL format. |
ACL Writing Style
Use this on the manuscript itself. ACL reviewers are NLP specialists who read
for whether the paper understands language as well as models; the style
that survives them is concrete, example-grounded, and precisely scoped.
First-page contract
- Open with the task or linguistic phenomenon, not the model family: what
goes in, what comes out, why it is hard, and for whom.
- State the contribution as a typed claim by paragraph two: new method, new
resource, new analysis, or new finding — ACL reviews are calibrated per type.
- Give one real example (input, desired output, failure of the status quo)
on page one; abstract problem statements without an example read as vague
at this venue.
- Say what languages the paper covers in the abstract if the answer is not
"English only" — and if it is, say that too.
Claim scoping in the LLM era
| Reflex phrasing | ACL-safe phrasing |
|---|
| "LLMs cannot do X" | "The five models tested fail X under these prompts" |
| "Our method understands Y" | "Improves the Y benchmark by n points; error classes A, B shrink" |
| "Works across languages" | "Evaluated on de/hi/sw/zh/ar; typological coverage discussed in §7" |
| "Significantly better" | Reserve for tested significance; give the test and p-value or interval |
| "State-of-the-art" | Scope to the exact setting, model scale, and date checked |
Reviewers increasingly ask whether a result is a property of the task, the
model snapshot, or the prompt; write so each claim names which.
Examples and error analysis as prose
- Every qualitative example must be attached to a number: how often the
illustrated behavior occurs, in which slice, under which condition.
Cherry-picked generations presented as evidence is a named reject pattern.
- Use interlinear glosses or transliteration conventions correctly for
non-English examples; sloppy linguistics costs credibility with exactly the
reviewers who like the paper's topic.
- Name error categories functionally ("negation-scope errors") rather than
narratively ("the model gets confused").
Anonymity-compatible voice
- Write self-reference in third person: "Smith (2024) introduced X," never
"In our previous work." Keep it in place until camera-ready.
- Do not cite "anonymous (under review)" material that reviewers cannot read;
ARR bars relying on documents unavailable to them.
- Acknowledgements, funding, and AI-assistance credits are omitted at
submission and added at camera-ready.
Compression into 8 (or 4) pages
- The short-paper form is a single sharp point with one strong experiment —
do not shrink a long paper into four pages; re-argue it.
- Push prompt dumps, per-language tables, and hyperparameter grids to the
appendix; keep one summary row of each in the body (see
acl-supplementary).
- Kill the related-work-as-inventory section; two paragraphs of positioned
contrast beat a page of citations (see
acl-related-work).
- Figures earn their space only when they carry an argument — pipeline
diagrams restating the text are the first cut.
Limitations and ethics prose
- Write Limitations as the referee brief against yourself: scope, data
coverage, model dependence, evaluation validity. Specificity here is
protected — ACL instructs reviewers not to penalize honest limitations.
- The optional ethics statement is for real stakes: human data, dual use,
representational harm. A boilerplate ethics paragraph is worse than none.
Micro-edit pass
weak: "We leverage powerful LLMs to achieve impressive gains."
strong: "Reranking with a 7B model cuts negation-scope errors from
31% to 12% of sampled failures (Table 4)."
weak: "Performance is good across all settings."
strong: "Gains hold on 4 of 5 languages; Swahili degrades (-1.2 F1),
which §7 traces to tokenizer fragmentation."
Terminology and notation discipline
- Pick one name per concept and hold it: a system called "our reranker,"
"the verifier," and "the LLM judge" in three sections reads as three
systems to a tired reviewer.
- Define task-specific terms at first use, even standard-seeming ones —
"hallucination," "faithfulness," and "robustness" each have three
incompatible literatures behind them.
- Dataset names get their citation at first mention and exact split names
thereafter ("XNLI dev-matched," not "the dev set").
- Numbers in prose match tables to the decimal; reviewers diff them.
- Language codes: introduce once (ISO 639), then use consistently in
tables, figures, and prose alike.
Section-level failure smells
- An introduction with no example → underspecified task (fix first).
- A method section narrating engineering chronology ("we first tried...")
→ rewrite as design with rationale.
- A results section that re-reads the table aloud → replace with claims the
table supports plus pointers into it.
- A conclusion introducing new claims → move them into results or delete;
ACL reviewers treat conclusions as summaries under oath.
Output format
[Style diagnosis] task-first / model-first / survey-ish / underspecified
[First-page fix] <one concrete rewrite>
[Overclaim list] <claim -> scoped version>
[Example-evidence gaps] <anecdotes lacking counts>
[Compression plan] <cut / move / merge>
