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- Do not cite "anonymous (under review)" material that reviewers cannot read;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>
1---2name: acl-writing-style3description: 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.4---56# ACL Writing Style78Use this on the manuscript itself. ACL reviewers are NLP specialists who read9for whether the paper understands *language* as well as *models*; the style10that survives them is concrete, example-grounded, and precisely scoped.1112## First-page contract1314- Open with the task or linguistic phenomenon, not the model family: what15 goes in, what comes out, why it is hard, and for whom.16- State the contribution as a typed claim by paragraph two: new method, new17 resource, new analysis, or new finding — ACL reviews are calibrated per type.18- Give one real example (input, desired output, failure of the status quo)19 on page one; abstract problem statements without an example read as vague20 at this venue.21- Say what languages the paper covers in the abstract if the answer is not22 "English only" — and if it is, say that too.2324## Claim scoping in the LLM era2526| Reflex phrasing | ACL-safe phrasing |27|---|---|28| "LLMs cannot do X" | "The five models tested fail X under these prompts" |29| "Our method understands Y" | "Improves the Y benchmark by n points; error classes A, B shrink" |30| "Works across languages" | "Evaluated on de/hi/sw/zh/ar; typological coverage discussed in §7" |31| "Significantly better" | Reserve for tested significance; give the test and p-value or interval |32| "State-of-the-art" | Scope to the exact setting, model scale, and date checked |3334Reviewers increasingly ask whether a result is a property of the task, the35model snapshot, or the prompt; write so each claim names which.3637## Examples and error analysis as prose3839- Every qualitative example must be attached to a number: how often the40 illustrated behavior occurs, in which slice, under which condition.41 Cherry-picked generations presented as evidence is a named reject pattern.42- Use interlinear glosses or transliteration conventions correctly for43 non-English examples; sloppy linguistics costs credibility with exactly the44 reviewers who like the paper's topic.45- Name error categories functionally ("negation-scope errors") rather than46 narratively ("the model gets confused").4748## Anonymity-compatible voice4950- Write self-reference in third person: "Smith (2024) introduced X," never51 "In our previous work." Keep it in place until camera-ready.52- Do not cite "anonymous (under review)" material that reviewers cannot read;53 ARR bars relying on documents unavailable to them.54- Acknowledgements, funding, and AI-assistance credits are omitted at55 submission and added at camera-ready.5657## Compression into 8 (or 4) pages5859- The short-paper form is a single sharp point with one strong experiment —60 do not shrink a long paper into four pages; re-argue it.61- Push prompt dumps, per-language tables, and hyperparameter grids to the62 appendix; keep one summary row of each in the body (see `acl-supplementary`).63- Kill the related-work-as-inventory section; two paragraphs of positioned64 contrast beat a page of citations (see `acl-related-work`).65- Figures earn their space only when they carry an argument — pipeline66 diagrams restating the text are the first cut.6768## Limitations and ethics prose6970- Write Limitations as the referee brief against yourself: scope, data71 coverage, model dependence, evaluation validity. Specificity here is72 protected — ACL instructs reviewers not to penalize honest limitations.73- The optional ethics statement is for real stakes: human data, dual use,74 representational harm. A boilerplate ethics paragraph is worse than none.7576## Micro-edit pass7778```text79weak: "We leverage powerful LLMs to achieve impressive gains."80strong: "Reranking with a 7B model cuts negation-scope errors from81 31% to 12% of sampled failures (Table 4)."8283weak: "Performance is good across all settings."84strong: "Gains hold on 4 of 5 languages; Swahili degrades (-1.2 F1),85 which §7 traces to tokenizer fragmentation."86```8788## Terminology and notation discipline8990- Pick one name per concept and hold it: a system called "our reranker,"91 "the verifier," and "the LLM judge" in three sections reads as three92 systems to a tired reviewer.93- Define task-specific terms at first use, even standard-seeming ones —94 "hallucination," "faithfulness," and "robustness" each have three95 incompatible literatures behind them.96- Dataset names get their citation at first mention and exact split names97 thereafter ("XNLI dev-matched," not "the dev set").98- Numbers in prose match tables to the decimal; reviewers diff them.99- Language codes: introduce once (ISO 639), then use consistently in100 tables, figures, and prose alike.101102## Section-level failure smells103104- An introduction with no example → underspecified task (fix first).105- A method section narrating engineering chronology ("we first tried...")106 → rewrite as design with rationale.107- A results section that re-reads the table aloud → replace with claims the108 table supports plus pointers into it.109- A conclusion introducing new claims → move them into results or delete;110 ACL reviewers treat conclusions as summaries under oath.111112## Output format113114```text115[Style diagnosis] task-first / model-first / survey-ish / underspecified116[First-page fix] <one concrete rewrite>117[Overclaim list] <claim -> scoped version>118[Example-evidence gaps] <anecdotes lacking counts>119[Compression plan] <cut / move / merge>120```