Comparing Papers with AI? Start with a Shared Set of Fields
A common way to read with AI is to give it three papers separately and ask for a summary of each. The result is often three pieces of text that do not line up: one describes dataset size in detail, while another focuses on architecture. Both claim better performance, but a closer look reveals that they use different baselines.
For a useful comparison, do not leave extraction entirely open-ended. Establish a shared measuring stick—a set of comparison fields—so similarities and differences can occupy the same table.
In EasyLaTeX, open the target paper in your literature library and extract information one paper at a time through AI on the literature reading page. To add other papers to the same conversation, upload the relevant attachments and ask AI to list the papers it can actually read in this turn. Do not assume that the whole library, reading history from other pages, or files in other projects automatically enter the current conversation. Give each paper a consistent name or identifier, then combine the checked results in one comparison table.
Define fields shared by all papers
The following six fields provide a common starting point. Add or remove fields to fit your research question rather than following the template mechanically.
| Field | What AI should extract | Where to verify it in the paper |
|---|---|---|
| Research question | One sentence, preserving the original wording where possible | Abstract or final paragraph of the introduction |
| Method | Model or method name plus the core improvement or setting | Main equations or architecture explanation in the methods section |
| Dataset and scale | Dataset name, sample count, and training/test split | Dataset description in the experiments section |
| Evaluation metric | Metric name and its exact definition | Metric description in the experiments section |
| Main conclusion | One sentence capturing the main finding, not just repeating numbers | Conclusion or final sentences of the abstract |
| Limitations | Limitations acknowledged by the authors | Discussion or future work |
The second column tells AI what to extract; the third tells you where to check the answer. Once you set the fields, keep their wording consistent during the extraction pass. Even changing a question word can make earlier papers less comparable.
Work on one paper at a time, then spot-check its sources
The most reliable rhythm is process one paper, verify its results, and then move to the next. Supply the shared fields in the current paper's AI conversation and request extraction from that paper only. When you switch papers, provide the same fields again, save the result, and combine it later. Even if you uploaded several attachments together, identify papers by name and handle them individually rather than asking for a blended answer.
In a long context, simultaneous extraction can mix sources: AI may put Paper A's dataset in Paper B's method cell. The resulting sentence can sound perfectly natural, so the error is visible only when checked against the original.
After each paper, randomly check two cells. Concrete numbers and source wording are encouraging signs; if you find invented phrasing or terms absent from the paper, inspect the remaining cells individually as well.
Each answer should include the paper name, page or section, and a short source quotation when necessary. Verify these locations in the PDF. If the full text is unavailable or a relevant page has not been parsed, label the result “Not yet verified.” Do not turn “not read” into “not reported in the paper.”
Record metric protocols instead of forcing numbers to align
Two papers may both report accuracy while one evaluates a subset and the other evaluates the full dataset. Putting the numbers side by side without that context can falsely suggest that the first model is stronger.
State the evaluation scope in the same cell—for example, Accuracy (subset) and Accuracy (full). If the definitions themselves differ, use separate rows rather than merging them merely to make the table look tidy.
The following standalone LaTeX example can be used when turning the comparison into a table for a paper or research proposal.
Put it in the main file of a separate EasyLaTeX test project and use the compile button to check it. When incorporating it into an existing paper, copy only the table and necessary packages, not another \documentclass or \begin{document}. The Not reported entries are demonstration placeholders: replace each with checked information. If you have not read the source, write “Not yet verified,” not a claim that the authors did not report it.
\documentclass{article}
\usepackage{booktabs,tabularx}
\begin{document}
\begin{table}[htbp]
\centering
\caption{Comparison of papers using shared extraction fields.}
\begin{tabularx}{\linewidth}{lXXX}
\toprule
Field & Paper A & Paper B & Paper C \\
\midrule
Research question & Not reported & Not reported & Not reported \\
Method & Not reported & Not reported & Not reported \\
Dataset and split & Not reported & Not reported & Not reported \\
Metric and protocol & Not reported & Not reported & Not reported \\
Main conclusion & Not reported & Not reported & Not reported \\
Limitations & Not reported & Not reported & Not reported \\
\bottomrule
\end{tabularx}
\end{table}
\end{document}
Turn the table into a useful literature review
With the table complete, read across a row to compare how papers address the same issue; read down a column to follow one paper's research story.
For Related Work, writing from the cells is often easier than rewriting several long summaries. For example: “All three studies evaluated the same benchmark dataset, but only Paper B changed the training split ratio.” This conveys more than “The experimental settings differ.” Rows with the clearest differences often provide the best material to develop in the review.
Too many blanks or nearly identical entries? Revisit the fields
Many empty cells usually point to one of three situations:
- You checked and the paper does not report the item. Record that accurately; an omission in one paper is not automatically a gap in the entire research field.
- The fields do not fit the paper type. For example, experimental-paper fields may not suit a theoretical analysis.
- The information is in another section. It may be present but outside the conventional location, causing AI to miss it.
Conversely, if a column's entries all look the same, the field may be too broad. If everyone uses a Transformer, the method name alone offers little distinction. Break the method field into finer questions, such as changes to attention or positional encoding.
Both widespread blanks and repetitive entries tell you to adapt the fields to this particular group of papers. Do not ask AI to fill gaps with generic statements just to make the table appear complete. That removes the very distinctions the comparison is meant to reveal.
Summary
- Fields first: Set shared fields before extracting information, and keep the wording consistent during the pass.
- One paper per round: Avoid mixing sources and spot-check two cells against the paper after each extraction.
- State the protocol: Keep differing evaluation conditions visible instead of forcing incompatible metrics into one cell.
- Adjust when needed: If many cells are empty or repetitive, refine or redesign the fields for these papers.
Before writing your next review, extract your core papers one by one on the literature reading page in EasyLaTeX. Define shared fields, verify the evidence, and then bring the results into your paper project's review and tables.