aiscientists-dev/academic-humanizer

 
 
 

우리가 이걸 만든 이유

우리 중 일부는 많은 논문과 연구비 제안서를 작성하고, 우리 팀은 AI를 활용해 도움을 주기 시작했습니다 초안. 문제는 AI 지원 초안이 “최근 몇 년간…” 같은 식으로 일반적이고 장황하게 나온다는 점입니다. 서두, 과장된 표현, 그리고 지나치게 긴 문장들. 또한 작가 본인의 목소리와는 거리가 멀어지기도 하고, 정밀한 장학금을 잃는 것은 상황에 따라 다릅니다.

"휴머마이저"라는 도구들이 있지만, 이들은 블로그와 마케팅을 위해 만들어졌습니다. 종이에 사용하거나 NSF 제안서이며, 정확성을 평탄화시키고 다른 모든 것을 포함해 평탄화시킵니다. 신중한 표현 학술 글쓰기의 의존은 가장 먼저 사라지는 것입니다.

그래서 우리만의 초안을 만들었죠. 보정을 위해 AI가 자신의 초안과 우리 팀의 초안을 비교하게 했습니다 승인된 논문과 자금 지원 제안서, 그리고 차이점을 손으로 검토했습니다. 별거 아니에요, 그리고 게임 리뷰, 탐지기 무력화, 가짜 신선함을 더하는 것이 아닙니다. 우리는 AI 지원을 원했습니다 저자의 목소리로 명확하게 읽을 수 있는 초고들, 번호, 인용문, 주장들이 남아 있어야 합니다 정확히 그대로.

윤리와 공개

이것은 명료성과 목소리를 위한 편집 도구로, 작가가 이전에 인정한 작업에 맞게 조정된 것입니다.

Why we built this

Some of us write a lot of papers and grant proposals, and our team started using AI to help with drafts. The problem is that AI-assisted drafts come out generic and verbose, with “In recent years…” openers, inflated phrasing, and over-long sentences. They also drift from the author’s own voice and lose the precision scholarship depends on.

There are tools called “humanizers,” but they are built for blogs and marketing. Run one on a paper or an NSF proposal and it flattens the precision along with everything else. The careful wording academic writing depends on is the first thing to go.

So we put together our own. To calibrate it, we had the AI compare its own drafts with our team’s accepted papers and funded proposals, and we went through the differences by hand. It is nothing fancy, and it is not about gaming review, defeating detectors, or adding fake novelty. We wanted AI-assisted drafts to read clearly and in the author’s own voice, with the numbers, citations, and claims left exactly as written.

Ethics and disclosure

This is an editing aid for clarity and voice, calibrated to an author’s own prior accepted work. It does not generate findings, invent data, or change citations, and it is not designed to evade AI-use detection. Using it does not remove your obligation to disclose AI assistance: always follow the disclosure policy of the venue you submit to.

See it work

[!CAUTION] Before (a generic AI draft):

In recent years, continual learning has attracted increasing attention and achieved remarkable success. However, existing methods still face crucial challenges. In this proposal, we propose a novel framework that leverages cutting-edge techniques to delve into these intricate problems, paving the way for a transformative paradigm that will revolutionize the field.

[!TIP] After (clear, in the author’s voice, with claims tied to evidence):

Continual learning matters, but today’s methods stay empirical and their principles are unclear. That limits reliability and progress. This proposal builds a principled framework on three fronts: adaptation, soft supervision, and cross-domain knowledge. We demonstrate it on autonomous driving and network management.

More before/after passes are in examples/before-after.md: a general