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Acl Related Work

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brycewang-stanford
acl-related-work

Use when positioning an ACL submission against the NLP literature, covering ACL Anthology citation practice, arXiv-versus-published version citation, concurrent LLM-era preprints, prior-cycle ARR resubmission overlap, anonymity-preserving self-citation, and the fast-moving baseline problem in computational linguistics.

Overview

Publisherbrycewang-stanford
RepositoryAwesome-Journal-Skills
Skill nameacl-related-work
Stars
1.2K
Forks
159
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by brycewang-stanford on GitHub. Read the source before you install it.

Installation

Install the Acl Related Work AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ACL-Skills/skills/acl-related-work
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git /tmp/Awesome-Journal-Skills
mkdir -p .claude/skills
cp -r /tmp/Awesome-Journal-Skills/ACL-Skills/skills/acl-related-work .claude/skills/acl-related-work
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Acl Related Work in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Acl Related Work on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Acl Related Work is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

ACL Related Work

Use this to audit positioning and citation hygiene. NLP moves fast enough that related-work sections age between ARR cycles; the section must prove the authors know the field as of this cycle, not as of the project's start.

The Anthology-first rule

  • When a paper exists in the ACL Anthology (or another archival venue), cite that version, not its arXiv preprint — Anthology entries are the community's citation of record and carry stable BibTeX.
  • The Anthology is fully open access (post-2016 materials under CC BY 4.0), so "couldn't access the published version" is never a defense here.
  • For findings-tier work, cite it as Findings of ACL/EMNLP — it is archival and reviewers treat miscrediting it as sloppiness.

Freshness pressure unique to this field

  • Expect at least one reviewer to name a baseline or dataset from the last two conference rounds. Sweep ACL, EMNLP, NAACL, EACL, TACL, and CL for the nearest neighbors published since your project began.
  • LLM-era concurrency is the norm: assume 2-4 similar preprints exist. Cite the ones findable at submission, contrast on technique rather than date, and avoid priority wars that reviewers cannot adjudicate.
  • If a newer model release makes your comparison set look dated mid-cycle, address it in the author response and plan the upgrade for camera-ready or resubmission — silence reads as unawareness.

Positioning moves that work at ACL

Contribution typeThe contrast reviewers want spelled out
MethodNearest prior method + the mechanism-level difference, not just scores
ResourceExisting datasets' coverage gaps: languages, domains, phenomena, license
Analysis / evaluationWhat prior evaluations measured incorrectly or could not see
MetricFailure cases of the incumbent metric, with correlation evidence
Theme-track paperHow the work answers the year's theme question (ACL 2026: explainability)

One paragraph of typed contrast per lane beats an undifferentiated citation inventory; ACL's compressed format cannot afford taxonomy for its own sake.

Self-citation and resubmission overlap

  • Cite your own prior work in third person ("Building on Smith (2024)...") so anonymity survives; heavy reliance on one group's unpublished thread is itself an anonymity leak.
  • If this is an ARR resubmission, the revision note — not the related-work section — is where you discuss the previous version; the paper itself should read cleanly to a fresh reviewer.
  • Workshop and non-archival versions (most *ACL workshops are archival — check!) must be declared under the dual-submission policy rather than quietly extended.

Preprint-era eligibility judgment calls

  • Your own arXiv preprint of this submission is allowed (no anonymity period since January 2024), but the declared preprint status must match reality.
  • A competing preprint that appeared after your ARR submission date is concurrent work: cite it in camera-ready, note the timing, contrast honestly.
  • Do not cite an inaccessible or anonymized document as supporting evidence — ARR forbids leaning on materials reviewers cannot read.

Two-pass audit procedure

text
Pass 1 (coverage): for each claim of novelty, list the 3 nearest works
  from Anthology search + recent cycles; verify each is cited and
  contrasted, not just listed.
Pass 2 (hygiene): for each bib entry, check venue-of-record vs arXiv,
  Findings attribution, third-person self-citation, and that no cited
  item is reviewer-inaccessible.

Where to actually search, in order

  1. ACL Anthology full-text/venue search — the canonical record for every *ACL main conference, Findings volume, TACL, CL, and most workshops.
  2. The most recent two conference rounds' accepted-paper lists (each edition's site publishes them) — the freshness sweep reviewers run.
  3. Semantic Scholar / Google Scholar citation graphs walked forward from your three nearest neighbors — who already built on them?
  4. arXiv cs.CL for the concurrent-preprint scan, dated against your cycle's submission deadline so "concurrent" is defensible.
  5. Leaderboards and dataset pages for your benchmarks — the SOTA row often names an uncited system.

Related-work paragraph shape that survives compression

For each literature lane, three sentences: (1) what the lane established, with its two or three best citations; (2) the specific gap or assumption it left; (3) how this paper differs mechanically. Delete any sentence that neither cites nor contrasts. In a short paper, the whole section may be one such paragraph plus contrasts woven into the introduction — that is normal and reviewers at ACL expect it.

Red flags reviewers name explicitly

  • "The authors seem unaware of X" — the fatal version of staleness; one missed obvious neighbor outweighs fifty dutiful citations.
  • Citing a paper for a claim it does not make (checked more often than authors expect, since reviewers know their own subfield's papers).
  • A wall of "recently, LLMs have..." citations with no load-bearing role.
  • Comparing against numbers from a cited paper's arXiv v1 that changed in the published version.

Output format

text
[Positioning verdict] sharp / inventory-like / stale / overlapping
[Nearest neighbors] <3 works -> stated distinction each>
[Freshness risks] <recent baselines or datasets unaddressed>
[Citation hygiene] <arXiv-vs-Anthology, Findings credit, self-citation voice>
[Overlap declarations] <resubmission / workshop / preprint items to declare>

Frequently asked questions

What does the Acl Related Work AI skill do?

Use when positioning an ACL submission against the NLP literature, covering ACL Anthology citation practice, arXiv-versus-published version citation, concurrent LLM-era preprints, prior-cycle ARR resubmission overlap, anonymity-preserving self-citation, and the fast-moving baseline problem in computational linguistics.

Why use Acl Related Work on TypingMind?

Because you install it once and use it with any model. Acl Related Work is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Acl Related Work in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ACL-Skills/skills/acl-related-work. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Acl Related Work?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Acl Related Work?

As many as you like. As long as a model supports skills, you can use Acl Related Work with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Acl Related Work AI skill free?

Yes. It is published on GitHub by brycewang-stanford under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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