Introduction | The content is fine, but it reads “too much like AI writing”—full of boilerplate, templated sentence patterns, and long sentences. Reviewers are increasingly sensitive to these traces. This article explains how to clean them up before submission.
I. “AI flavor” Is Something Reviewers Now Watch For
A few years ago, readers couldn't yet tell AI text apart; now reviewers read several manuscripts a day and are already highly sensitive to certain writing patterns:
• Every paragraph starts with “First... Second... Finally...”
• Full of “It is worth noting that” and “In summary”
• Constant “significant improvement” and “extremely important”
• Sentences run 70 to 80 characters, making them hard to breathe through
These traces do not violate any rules in themselves, but they plant an idea: Did the author not write this carefully? That idea is often more fatal than flaws in the content.
II. Six High-Frequency Problem Types
| Type | What It Looks Like | How to Fix It |
| Boilerplate transitions | “In summary,” “It is worth noting that” | Delete them, or replace them with specific information to connect ideas |
| Empty emphasis | “extremely important,” “significant improvement” | Replace with specific numbers |
| Templated paragraphs | Every paragraph goes “First... Second... Finally” | Break up the structure |
| Translationese | “perform an operation of...” | Restore it to a normal verb |
| Stacked parallelism | Three-clause bombardment | Keep only the strongest sentence |
| Overlong sentences | One sentence of 70 to 80 characters | Split into two or three sentences |
The first two types do the most damage to credibility—when reviewers see phrases like “significant improvement,” they can tell at a glance that you lack confidence.
III. Why Use a Rule Engine Instead of Asking the Large Model Again
Many people's first reaction is “let the large model revise it itself.” The problem is: when the same model judges its own work, it protects itself—it thinks it wrote quite smoothly.
MuchSci Paper Sailor comes with a de-AI-flavor detection engine that uses rules for detection (without calling a large model): reproducible, locatable, consistent results every time for the same passage, and it can pinpoint exactly which sentence triggered a hit.
IV. How to Use the AI-Flavor Index
The detection results give you three things:
1. AI-flavor index: see at a glance how strong the “flavor” of the manuscript is.
2. Hit locations: down to the paragraph and sentence.
3. Revision suggestions: each item gives a direction for revision.
Then handle them by severity: high items must be reduced to zero. Handle medium and low items as appropriate, but not a single high item can remain—that is where they are easiest to recognize at a glance.
V. A Complete Walkthrough
1. Detect first to get the index and hit list.
2. Revise item by item by location, rather than overturning the whole text.
3. Recheck to see whether the index has dropped and high items are at zero.
4. Repeat until high items reach zero.
5. Then proceed to review, avoiding having to review everything again after revisions.
VI. Before-and-After Comparison
Empty emphasis:
• Before: This method achieved extremely significant performance improvements on multiple datasets.
• After: This method improves performance by 2.1, 1.7, and 2.4 percentage points on 3 datasets, respectively.
Translationese:
• Before: We performed an operation of preprocessing on the data.
• After: We first preprocessed the data.
Templated paragraphs + overlong sentences:
• Before: In summary, considering factors from multiple aspects, in order to better improve the model's overall performance, after conducting a large number of experiments, we ultimately adopted such a redesigned network structure.
• After: To improve performance, we redesigned the network structure.
VII. Common Pitfalls
• Removing the AI flavor at the cost of the meaning. Rephrase; do not delete information.
• Only swapping words without changing sentences. Changing “significant improvement” to “substantial improvement” does not reduce the flavor at all.
• Doing it after review. If you revise afterward, you will have to review it again—a complete waste.
• Looking only at the index and not the locations. The index is the result; the locations are where you can actually take action.
VIII. Conclusion
AI flavor is not mysticism; it is a set of enumerable, locatable language features. MuchSci Paper Sailor's de-AI-flavor detection engine provides the index, locations, and revision suggestions—what you need to do is revise item by item until high items reach zero.
Pre-submission self-check:
• Has the manuscript been run through AI-flavor detection?
• Are high items at zero?
• Are there still empty phrases like “significant / extremely / in summary” left?