Polishes academic English paragraph by paragraph, reviewing grammar, word choice, voice, coherence, and sentence structure. Outputs revision suggestions alongside polished text. Triggered by phrases like 'polish this paragraph,' 'check the grammar,' 'rewrite in academic English,' or keywords like manuscript editing, SCI polishing, and journal submission editing.
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Helps users review and polish English papers paragraph by paragraph according to international academic journal standards. Covers grammar correction, academic word choice optimization, voice normalization, coherence strengthening, sentence variety, and more. Outputs revision suggestions along with the polished text.
Users only need to provide:
Example:
"Polish this Introduction for me. The target venue is NeurIPS, and I'd like the language to sound more natural with better logical flow."
Polishing is carried out across 5 dimensions. Each dimension is rated independently with specific revision suggestions:
| Dimension | Label | Review Focus |
|---|---|---|
| Grammar | Grammar | Subject-verb agreement, tense, articles, prepositions, clause structure, punctuation |
| Word Choice | Word Choice | Academic register, precision, collocation, avoidance of colloquialisms |
| Voice | Voice & Tense | Active/passive voice selection, tense consistency |
| Coherence | Coherence & Cohesion | Intra-paragraph and inter-paragraph transitions, argumentation chain, use of signaling words |
| Sentence Structure | Sentence Structure | Sentence variety, balance of long and short sentences, coordination and subordination |
| Error Type | Incorrect Example | Correction | Explanation |
|---|---|---|---|
| Subject-verb disagreement | The results of the experiment shows... | The results of the experiment show... | Subject is "results" (plural) |
| Missing/misused article | We propose method to solve... | We propose a method to solve... | Singular countable nouns require an article |
| Dangling modifier | Using the proposed method, the accuracy was improved. | Using the proposed method, we improved the accuracy. | The implied subject of a participial phrase must match the main clause subject |
| Run-on sentence | The model performs well , it achieves 95% accuracy. | The model performs well**;** it achieves 95% accuracy. / The model performs well**. It** achieves 95% accuracy. | A comma cannot join two independent clauses |
| Incomplete comparison | Our method is more efficient. | Our method is more efficient than the baseline. | Comparatives require an explicit object of comparison |
| Broken parallelism | The system can detect, classify, and is able to segment... | The system can detect, classify, and segment... | Coordinated elements must share the same grammatical form |
| that/which confusion | The model which we proposed... | The model that we proposed... | Restrictive relative clauses use "that" |
| Irregular plurals | These phenomenon indicate... | These phenomena indicate... | Watch for irregular plural forms |
| Rule | Correct Usage | Common Mistake |
|---|---|---|
| Serial comma (Oxford comma) | A, B**,** and C | A, B and C (the Oxford comma is recommended in academic writing) |
| Em dash | We used three models — A, B, and C — for comparison. | An em dash with spaces on both sides, or without spaces (depends on journal style) |
| Capitalization after colon | Capitalize if a complete sentence follows: The result is clear: The model outperforms... | Do not capitalize if a fragment follows |
| Quotation marks and periods | American style: period inside quotes. British style: period outside quotes. | Choose based on the target journal's regional convention |
| Abbreviation periods | e.g., i.e., et al., etc. | Note the comma: e.g., / i.e., |
| Section | Recommended Tense | Example |
|---|---|---|
| Abstract | Past tense (what was done) + present tense (conclusions) | "We proposed a method... The results show that..." |
| Introduction | Present tense (current knowledge/consensus) + past tense (prior work) | "Deep learning has become... Smith et al. demonstrated that..." |
| Methods | Past tense (experimental procedures) | "We trained the model on... The data were preprocessed..." |
| Results | Past tense (experimental findings) | "The model achieved 95% accuracy. Table 2 shows..." |
| Discussion | Present tense (interpreting significance) + past tense (citing results) | "This result suggests that... Our findings indicated that..." |
| Conclusion | Past tense (summarizing work) + present tense (contributions/significance) | "We proposed and evaluated... This work contributes to..." |
| Colloquial | Academic Alternative | Context Notes |
|---|---|---|
| a lot of | numerous / a substantial number of / considerable | Choose based on what is being modified |
| get | obtain / acquire / achieve / attain | Choose based on collocation |
| show | demonstrate / illustrate / indicate / reveal | "demonstrate" emphasizes proof; "indicate" emphasizes suggestion |
| big / huge | substantial / significant / considerable | |
| thing | factor / aspect / element / component | |
| good | effective / favorable / advantageous / robust | |
| bad | adverse / detrimental / suboptimal / inferior | |
| use | employ / utilize / leverage / adopt | "utilize" is more formal than "use"; "leverage" emphasizes exploiting an advantage |
| about | approximately / roughly / circa | Use "approximately" for numerical descriptions |
| try | attempt / endeavor | |
| look at | examine / investigate / analyze / explore | |
| find out | determine / ascertain / identify / discover | |
| go up / go down | increase / decrease / rise / decline | |
| point out | highlight / emphasize / underscore | |
| deal with | address / tackle / handle / mitigate | |
| make sure | ensure / verify / confirm | |
| kind of / sort of | somewhat / to some extent / partially | |
| start / begin | initiate / commence / undertake | |
| end / finish | conclude / terminate / complete | |
| help | facilitate / enable / assist / contribute to | |
| need | require / necessitate | |
| can | is capable of / is able to / has the potential to | Avoid over-substitution — "can" is acceptable in academic writing |
| Vague Expression | Precise Alternative | Notes |
|---|---|---|
| very good results | statistically significant improvement / a 12% increase in accuracy | Replace vague modifiers with concrete data |
| some researchers | Several studies (Chen et al., 2023; Li et al., 2024) | Replace vague references with specific citations |
| recently | In the past five years / Since 2020 | Provide a time range |
| a few | three / a small number of (n=3) | Specify the quantity |
| it is known that | Prior work has established that (citation) | Support with a citation |
| this is important | This is critical for / This has significant implications for | Explain why it matters |
| Redundant Expression | Concise Version |
|---|---|
| in order to | to |
| due to the fact that | because / since |
| at the present time | currently / now |
| it is worth noting that | Notably, / Note that |
| it should be pointed out that | (state the content directly) |
| a total of 50 samples | 50 samples |
| the vast majority of | most |
| in the event that | if |
| has the ability to | can |
| on a daily basis | daily |
| in close proximity to | near |
| take into consideration | consider |
| is in agreement with | agrees with |
| serves the function of | functions as |
| Scenario | Recommended Voice | Example |
|---|---|---|
| Describing the authors' actions | Active (We) | We trained the model using... |
| Describing general methods/established facts | Passive | The data were collected from... |
| Emphasizing the object of an action | Passive | The samples were analyzed using mass spectrometry. |
| Reporting results | Prefer active | Our method achieves 95% accuracy. |
| Describing equipment/materials | Passive | The solution was heated to 100°C. |
| Issue | Incorrect Example | Correction |
|---|---|---|
| Overuse of passive | It was found by us that the results were improved by the method. | We found that our method improved the results. |
| Inconsistent person | The author proposes... We then evaluate... | Use "We" or "The authors" consistently |
| Meaningless passive | It can be seen that accuracy increases. | Accuracy increases. / The results show that accuracy increases. |
| Person | Use Case | Notes |
|---|---|---|
| We | Describing the authors' own work (most common) | Many journals accept "We" even for single-author papers |
| The authors | A more formal alternative | Some journals prefer this usage |
| I | Single-author theses and dissertations | Some journals do not accept this |
| One | Generic/hypothetical statements | Somewhat old-fashioned; less common in modern academic writing |
| Logical Relationship | Signal Words/Phrases | Example |
|---|---|---|
| Addition | Furthermore, Moreover, Additionally, In addition | Furthermore, our method generalizes well to unseen data. |
| Contrast | However, In contrast, Conversely, On the other hand, Nevertheless | However, this approach suffers from high computational cost. |
| Cause & Effect | Therefore, Consequently, As a result, Hence, Thus | Therefore, we adopt a two-stage training strategy. |
| Exemplification | For example, For instance, Specifically, In particular | Specifically, we focus on the image classification task. |
| Emphasis | Indeed, Notably, Importantly, It is worth noting that | Notably, the improvement is consistent across all datasets. |
| Concession | Although, Despite, Notwithstanding, While, Even though | Although the model is simple, it achieves competitive results. |
| Summary | In summary, To summarize, Overall, In conclusion | Overall, the proposed method outperforms existing baselines. |
| Qualification | Yet, Still, Nonetheless, That said | That said, there are several limitations to our approach. |
| Sequence | First, Second, Finally, Subsequently, Then | First, we preprocess the data. Subsequently, we train the model. |
| Condition | If, Provided that, Given that, Assuming that | Given that the dataset is imbalanced, we apply oversampling. |
| Pattern | Description | Example Opening Sentence |
|---|---|---|
| Hook | The end of one paragraph leads into the next topic | "This raises the question of how to efficiently scale the model." |
| Recap | The next paragraph opens by revisiting the prior conclusion | "Having established the effectiveness of our approach, we now turn to..." |
| Contrast Bridge | Points out the shortcomings of the prior approach, introducing the current one | "While these methods achieve reasonable accuracy, they fail to address..." |
| Question Bridge | Uses a question to create a transition | "How can we overcome this limitation? In this section, we propose..." |
| Topic Sentence | The first sentence of each paragraph summarizes the core argument | "The key advantage of our method is its ability to..." |
| Problem | Description | Fix Strategy |
|---|---|---|
| Jumping argumentation | Leaping from A to C without the B step | Add intermediate reasoning steps or transitional sentences |
| Signal word overuse | Starting every sentence with However / Moreover | Reduce signal words; use sentence structure to convey logic |
| Signal word misuse | Using "Furthermore" to express contrast | Use "However" for contrast; "Furthermore" for addition |
| Overly long paragraphs | A single paragraph exceeding 8–10 sentences | Split into 2–3 paragraphs by argument point |
| Overly short paragraphs | A paragraph with only 1–2 sentences | Merge into a related paragraph or expand the discussion |
| Unclear reference | "This shows..." — what does "this" refer to? | "This result shows..." / "This finding indicates..." |
| Strategy | Original | Improved |
|---|---|---|
| Participial phrase opening | We use attention mechanism, and we improve accuracy. | Leveraging the attention mechanism, we improve accuracy. |
| Inversion for emphasis | The improvement is particularly notable in low-resource settings. | Particularly notable is the improvement in low-resource settings. |
| Appositive insertion | The model, which was proposed by Smith, achieves... | The model, proposed by Smith (2023), achieves... |
| Nominalization | We improved the model, and this led to... | The improvement of the model led to... |
| Parallel structure | The method is fast. It is also accurate. It is scalable too. | The method is fast, accurate, and scalable. |
| Fronted adverbial | Accuracy improved significantly when we added data augmentation. | With data augmentation, accuracy improved significantly. |
| Problem | Example | Fix |
|---|---|---|
| Overly long sentences (>40 words) | We trained the model on the dataset which was collected from ... and preprocessed using ... and then evaluated on ... | Split into 2–3 shorter sentences |
| Consecutive short sentences | The accuracy is high. The model is fast. It uses less memory. | Combine: The model achieves high accuracy with fast inference and low memory consumption. |
| Starting with There is/are | There are many studies that focus on... | Many studies focus on... |
| Overuse of It is...that cleft sentences | It is the attention mechanism that improves... | The attention mechanism improves... |
| Noun pile-ups | deep learning image classification model performance | the performance of a deep learning model for image classification |
For each paragraph of text provided by the user, output in the following format:
### Original
[User's original text]
### Review
| Dimension | Rating | Key Issues |
|-----------|--------|-----------|
| Grammar | ✓ Good / △ Needs improvement / ✗ Significant issues | Brief description |
| Word Choice | ✓ / △ / ✗ | Brief description |
| Voice | ✓ / △ / ✗ | Brief description |
| Coherence | ✓ / △ / ✗ | Brief description |
| Sentence Structure | ✓ / △ / ✗ | Brief description |
### Detailed Changes
1. **Original**: "..."
**Revised**: "..."
**Reason**: [Specific rationale citing the rules above]
2. ...
### Polished Version
[Complete polished paragraph]Different disciplines have their own writing conventions. Respect field-specific norms when polishing:
| Field | Characteristics | Notes |
|---|---|---|
| Computer Science | Active voice ("We") is common | More colloquial phrasing is tolerated (e.g., "we run"); algorithm descriptions must be precise |
| Medicine/Biology | Passive voice predominates | "Patients were randomized..."; terminology must conform to MeSH standards |
| Physics | Concise, equation-driven | Mathematical derivations must be rigorous; "one can show that..." is common |
| Social Sciences | Frequent use of hedging | "may," "might," "suggests"; avoid overly absolute statements |
| Engineering | Results-oriented | Emphasis on performance metrics and experimental validation |
When the user submits text for polishing, follow this workflow:
Core Principles:
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