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Academic writing tips.how to write clearly and concisely in technical papers (59)

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Starting 15 pts 0 followers
Manipal Institute of Technology · Posted
Renewable Energy Science Communication Materials Science

My Setup

I run a machine learning lab with 8 PhD students. Over the past 18 months, I have noticed a significant shift in how my students (and I) work, largely driven by large language models.

Things that have genuinely improved:

  • Literature search: I use LLMs to get a quick conceptual map before diving into papers
  • Code debugging: GitHub Copilot has reduced boilerplate time by ~30%
  • Writing: Grammar and clarity checks are now instant

Things I am cautious about:

  • Students are sometimes getting confidently wrong answers about recent results
  • Risk of AI-generated text without proper attribution
  • I worry that relying on AI for literature summarization may weaken reading comprehension skills

The question: How are others balancing the productivity benefits against these risks? And what explicit policies has your department adopted for AI use in research?

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32 Replies

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0
Deepa Krishnamurthy Distinguished 810 pts · Accepted answer

Great question.I went through something very similar in my second year.

The key insight for me was that LoRA is actually quite well-suited for NER tasks, especially in low-resource settings. I would recommend:

  1. Use LoRA with r=8 or r=16.don't go higher for 8K samples
  2. Apply LoRA to attention layers only, not the feed-forward layers
  3. Use a cosine learning rate schedule with warm-up (10% of steps)

For Telugu-English code-mixed NER specifically, you might also look at MuRIL.it is pretrained on Indian language data and often outperforms XLM-R on Indic tasks even with less fine-tuning data.

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Sunil Bhattacharya Active 390 pts · Accepted answer

Highly recommend checking out the PMRF portal for the actual numbers.they update the stipend structure annually.

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Royal Dsouza Starting 30 pts · Accepted answer

The short answer: it depends heavily on the journal and field, but here is a general framework.

If your null result is well-powered and the hypothesis was reasonable: Absolutely publish it. Null results in well-designed studies are as valuable as positive results. Journals like PLOS ONE explicitly welcome them.

If your study is underpowered: Be transparent about it. Report effect sizes and confidence intervals, not just p-values. A small, underpowered study with honest reporting is still a contribution.

What kills papers is when limitations are obvious but the authors have not addressed them.

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Disha Malhotra Starting 8 pts · Accepted answer

I have successfully received PMRF (lateral entry, second year). Happy to share what worked in my proposal.

On the research proposal:

  • 60–70% technical approach, 30% context and impact
  • Preliminary results absolutely help. Coursework projects are fine if genuinely relevant
  • The societal impact section should be substantive.PMRF is a national initiative; reviewers want to see why this matters

On citations: Yes, cite your MTech thesis. Framing your PhD as an extension of prior work you did shows continuity of thought.

0
Bhavana Reddy Starting 30 pts · Accepted answer

Great question.I went through something very similar in my second year.

The key insight for me was that LoRA is actually quite well-suited for NER tasks, especially in low-resource settings. I would recommend:

  1. Use LoRA with r=8 or r=16.don't go higher for 8K samples
  2. Apply LoRA to attention layers only, not the feed-forward layers
  3. Use a cosine learning rate schedule with warm-up (10% of steps)

For Telugu-English code-mixed NER specifically, you might also look at MuRIL.it is pretrained on Indian language data and often outperforms XLM-R on Indic tasks even with less fine-tuning data.

0
Varsha Pillai Growing 60 pts · Accepted answer

CSIR-UGC NET preparation: past papers are the most important resource. Everything else is secondary.

0
Geeta Rao · Accepted answer

For Zotero vs BibTeX management, I moved to Zotero + Better BibTeX 3 years ago and have not looked back.

My workflow:

  1. Zotero browser connector captures papers instantly from ArXiv, Google Scholar, ACM DL, IEEE Xplore
  2. Better BibTeX generates a clean .bib file that auto-updates whenever I add a paper
  3. Overleaf directly pulls from the .bib file via Dropbox sync

One tip: create a Zotero collection per paper/project. When you export the .bib, export only that collection to keep it clean.

0
Sowmya Narayanan Active 310 pts · Accepted answer

I am in a very similar situation. Would you be willing to share the outline of your PMRF proposal? Not the content.just the section headings and approximate word allocation.

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Replying to Sowmya Narayanan
Ashwin Murthy Distinguished 560 pts · Accepted answer

For longitudinal data with missing values, mixed-effects models handle this more gracefully than imputation in most cases.

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Replying to Sowmya Narayanan
Lalitha Mohan Active 230 pts · Accepted answer

When you say 'multiple runs with confidence intervals', do you mean running the full training pipeline multiple times with different random seeds and reporting mean ± std? Or something more statistically rigorous?

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Replying to Sowmya Narayanan
Rohan Desai Distinguished 950 pts · Accepted answer

Marking this as the accepted answer. Incredibly helpful and specific. Really appreciate you taking the time.

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Harini Balakrishnan Active 215 pts · Accepted answer

I'd push back slightly on the idea that industry collaboration is universally beneficial for academic research. The incentive misalignment is real.

Industry partners want deliverables on a schedule. Academic research is often exploratory and unpredictable. When the two clash, it is usually the researcher who has to compromise.either by rushing results, constraining publication, or steering away from results that are inconvenient for the sponsor.

This doesn't mean industry collaboration is bad. But the terms matter enormously. IP rights, publication rights, and the ability to pursue negative results should be negotiated upfront and protected in writing.

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Xena D'Souza Starting 25 pts · Accepted answer

I found that writing a paper abstract first (before the paper) and then reverse-engineering the paper from the abstract helped with clarity enormously.

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Wajid Khan Growing 110 pts · Accepted answer

Overleaf + GitHub integration is underrated. You get version control and collaboration in one place.

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Meghana Rao Growing 170 pts · Accepted answer

Could you clarify what you mean by results-blind review? I have heard of it but never understood how it works in practice. Wouldn't reviewers need to see results to evaluate whether the methodology is sound?

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Hema Suresh Starting 20 pts · Accepted answer

Seconding the recommendation for Zotero. Game changer for managing references across multiple projects.

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Meena Sharma · Accepted answer

I'd push back slightly on the idea that industry collaboration is universally beneficial for academic research. The incentive misalignment is real.

Industry partners want deliverables on a schedule. Academic research is often exploratory and unpredictable. When the two clash, it is usually the researcher who has to compromise.either by rushing results, constraining publication, or steering away from results that are inconvenient for the sponsor.

This doesn't mean industry collaboration is bad. But the terms matter enormously. IP rights, publication rights, and the ability to pursue negative results should be negotiated upfront and protected in writing.

0
Lakshmi Devi Growing 75 pts · Accepted answer

Respectfully, I think this framing misses the systemic dimension. Individual researchers cannot solve a collective action problem. If you submit to open-access journals and your colleague submits to Nature, your colleague gets the promotion. Until evaluation criteria change at the institutional level.which requires policy intervention.individual choices have minimal impact.

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Replying to Lakshmi Devi
Hema Suresh Starting 20 pts · Accepted answer

For longitudinal data with missing values, mixed-effects models handle this more gracefully than imputation in most cases.

0
Replying to Lakshmi Devi
Varsha Pillai Growing 60 pts · Accepted answer

If you are not already using a reading group format with your students, I would strongly recommend it. Forces everyone to articulate what they read.

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Replying to Lakshmi Devi
Hema Suresh Starting 20 pts · Accepted answer

Could you clarify what you mean by results-blind review? I have heard of it but never understood how it works in practice. Wouldn't reviewers need to see results to evaluate whether the methodology is sound?

0
Rekha Shetty Starting 40 pts · Accepted answer

I have successfully received PMRF (lateral entry, second year). Happy to share what worked in my proposal.

On the research proposal:

  • 60–70% technical approach, 30% context and impact
  • Preliminary results absolutely help. Coursework projects are fine if genuinely relevant
  • The societal impact section should be substantive.PMRF is a national initiative; reviewers want to see why this matters

On citations: Yes, cite your MTech thesis. Framing your PhD as an extension of prior work you did shows continuity of thought.

0
Eshan Patil Growing 70 pts · Accepted answer

I can answer your question about IEEE Transactions review timelines from my experience submitting to IEEE TNNLS.

For Q1 IEEE journals, expect:

  • Initial desk review: 2–4 weeks
  • Full peer review: 3–6 months
  • Major revision decision: another 2–3 months for re-review

As for code and data, most IEEE journals now request code upon acceptance, not submission. Regarding your 2.3% improvement.that is borderline. You will need very strong experimental rigor: multiple runs, confidence intervals, and significance tests.

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test tester Growing 165 pts · Accepted answer

Overleaf + GitHub integration is underrated. You get version control and collaboration in one place.

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Replying to test tester
Geeta Rao · Accepted answer

Thank you! The reference to MuRIL is particularly useful.I had not considered it as an alternative to XLM-R for Indic languages.

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Replying to test tester
Royal Dsouza Starting 30 pts · Accepted answer

+1 to everything said above. My experience was identical.

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Replying to test tester
Varsha Pillai Growing 60 pts · Accepted answer

This is exactly what I needed to hear. One clarifying question: you mentioned FedProx handles non-IID data better.does that mean I should switch to FedProx by default, or only if I observe poor convergence with FedAvg first?

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test tester Growing 165 pts · Accepted answer

For Zotero vs BibTeX management, I moved to Zotero + Better BibTeX 3 years ago and have not looked back.

My workflow:

  1. Zotero browser connector captures papers instantly from ArXiv, Google Scholar, ACM DL, IEEE Xplore
  2. Better BibTeX generates a clean .bib file that auto-updates whenever I add a paper
  3. Overleaf directly pulls from the .bib file via Dropbox sync

One tip: create a Zotero collection per paper/project. When you export the .bib, export only that collection to keep it clean.

0
Replying to test tester
Chetan Jain Growing 100 pts · Accepted answer

For longitudinal data with missing values, mixed-effects models handle this more gracefully than imputation in most cases.

0
Replying to test tester
Vikram Bhatia Active 420 pts · Accepted answer

This question comes up a lot. The answer really depends on your specific field and what your committee values.

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Manish Tiwari Starting 10 pts · Accepted answer

For Zotero vs BibTeX management, I moved to Zotero + Better BibTeX 3 years ago and have not looked back.

My workflow:

  1. Zotero browser connector captures papers instantly from ArXiv, Google Scholar, ACM DL, IEEE Xplore
  2. Better BibTeX generates a clean .bib file that auto-updates whenever I add a paper
  3. Overleaf directly pulls from the .bib file via Dropbox sync

One tip: create a Zotero collection per paper/project. When you export the .bib, export only that collection to keep it clean.

0
Jagdish Rawat · Accepted answer

The Fulbright SoP is very different from a typical PhD SoP. It needs to focus on cultural exchange and diplomacy, not just research.