47/ 50
＋ Outputs only concise, consistent BibTeX entries.
－ Adds the He paper DOI despite its absence from the input.
Clean BibTeX Formatter for Academic Projects
I am preparing a BibTeX file for an academic project.
| Category | Study › Papers |
|---|---|
| Tags | ReformattingResearcherTemplate |
I am preparing a BibTeX file for an academic project. Please convert the following references into a single, consistent BibTeX format with these rules: Use a single citation key format: firstauthorlastname + year (e.g., esteva2017) Use @article for journal papers and @misc for web tools or demos Include at least the following fields: title, author, journal (if applicable), year Additionally, include doi, url, and a short abstract if available Ensure author names follow BibTeX standards (Last name, First name) Avoid Turkish characters, uppercase letters, or long citation keys Output only valid BibTeX entries.
What this prompt does
Use this to clean BibTeX files for academic projects. It separates journal papers and web tools into article and misc entries, avoiding Turkish characters and long keys.
Model comparison
ChatGPT is strongest in format, concision, and accuracy. Gemini is detailed but seriously violates the output constraint, while [C] is missing.
22/ 50
＋ Provides detailed author names and bibliographic metadata.
－ Violates output-only rules with prose, fences, and duplication.
| Criterion | ChatGPT | Gemini | Leader |
|---|---|---|---|
| Instruction following | 9 | 2 | ChatGPT +350% |
| Accuracy | 9 | 7 | ChatGPT +29% |
| Specificity | 9 | 8 | ChatGPT +13% |
| Structure | 10 | 4 | ChatGPT +150% |
| Right length | 10 | 1 | ChatGPT +900% |
Scored 1–10 by gpt-5.6-sol with model names hidden (2026-09-24). This is an AI review, not a measurement.
Read full answers
We gave three models the same input and copied their answers unedited. Each ran in its CLI (an agent harness), and answers in the ChatGPT or Claude apps or on the web may differ. Outputs are in Korean.
[Pasted material] 1. Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118. https://doi.org/10.1038/nature21056 2. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770-778. 3. Hugging Face. (2023). Transformers: State-of-the-art Natural Language Processing [Computer software]. https://huggingface.co/docs/transformers
@article{esteva2017, title = {Dermatologist-level classification of skin cancer with deep neural networks}, author = {Esteva, A. and Kuprel, B. and Novoa, R. A. and Ko, J. and Swetter, S. M. and Blau, H. M. and Thrun, S.}, journal = {Nature}, year = {2017}, volume = {542}, number = {7639}, pages = {115--118}, doi = {10.1038/nature21056}, url = {https://doi.org/10.1038/nature21056} }
@inproceedings{he2016, title = {Deep residual learning for image recognition}, author = {He, K. and Zhang, X. and Ren, S. and Sun, J.}, booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, year = {2016}, pages = {770--778}, doi = {10.1109/CVPR.2016.90}, url = {https://doi.org/10.1109/CVPR.2016.90} }
@misc{huggingface2023, title = {Transformers: State-of-the-art Natural Language Processing}, author = {{Hugging Face}}, year = {2023}, note = {Computer software}, url = {https://huggingface.co/docs/transformers} }
More in this category
| Draft a response to reviewer comments | |
| Academic Research Writer | |
| Academician | |
| Accounting Information System | |
| Act as a Senior Research Paper Evaluator |