| --- |
| pretty_name: ChemPro |
| license: cc-by-nc-4.0 |
| language: |
| - en |
| size_categories: |
| - 1K<n<10K |
| task_categories: |
| - question-answering |
| - multiple-choice |
| task_ids: |
| - multiple-choice-qa |
| tags: |
| - chemistry |
| - science |
| - benchmark |
| - evaluation |
| - reasoning |
| - curriculum |
| - arxiv:2602.03108 |
| configs: |
| - config_name: mcq |
| data_files: |
| - split: easy |
| path: data/mcq/easy.parquet |
| - split: medium |
| path: data/mcq/medium.parquet |
| - split: challenging |
| path: data/mcq/challenging.parquet |
| - split: difficult |
| path: data/mcq/difficult.parquet |
| - config_name: numerical |
| data_files: |
| - split: easy |
| path: data/numerical/easy.parquet |
| - split: medium |
| path: data/numerical/medium.parquet |
| - split: challenging |
| path: data/numerical/challenging.parquet |
| - split: difficult |
| path: data/numerical/difficult.parquet |
| --- |
| |
| # ChemPro |
|
|
| **A progressive chemistry benchmark for large language models.** |
|
|
| ChemPro is an evaluation benchmark. It contains **4,100 chemistry questions** in |
| four tiers of increasing difficulty. |
|
|
| Most benchmarks take their difficulty labels from annotator judgement. ChemPro |
| does not. Each tier comes from one established educational source: |
|
|
| - elementary web material |
| - Indian NCERT curricula for grades 9–10 and 11–12 |
| - JEE Mains competitive examinations |
|
|
| Decades of curriculum design set the difficulty of each tier. No one applied the |
| labels afterwards. |
|
|
| The benchmark keeps conceptual understanding separate from computational |
| reasoning. Multiple-choice questions test the first. Numerical questions test the |
| second. You can score the two independently. |
|
|
| ## Why the tiers matter |
|
|
| Many models score well on introductory chemistry. Their accuracy then drops |
| sharply as the questions become harder to read, even when the concepts stay |
| inside the same syllabus. |
|
|
| JEE Mains follows the official NCERT syllabus. The `challenging` and `difficult` |
| tiers therefore cover the same concepts. The accuracy gap between these two tiers |
| shows the effect of question complexity alone, not of subject coverage. |
|
|
| ## Contents |
|
|
| | Split | `tier` | Source | MCQ | Numerical | Total | |
| |---|---|---|---:|---:|---:| |
| | `easy` | `CP_E` | Web quizzes and questionnaires | 590 | 204 | 794 | |
| | `medium` | `CP_M` | NCERT, grades 9–10 | 229 | 107 | 336 | |
| | `challenging` | `CP_C` | NCERT, grades 11–12 | 455 | 208 | 663 | |
| | `difficult` | `CP_D` | JEE Mains, 2020–2024 | 1,493 | 814 | 2,307 | |
| | **Total** | | | **2,767** | **1,333** | **4,100** | |
|
|
| Each question also has one or more subfield labels: |
|
|
| | Subfield | Items | |
| |---|---:| |
| | Inorganic-Chemistry | 1,651 | |
| | Physical-Chemistry | 1,536 | |
| | Organic-Chemistry | 915 | |
| | Bio-Chemistry | 227 | |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import load_dataset |
| |
| # One tier of multiple-choice questions |
| mcq = load_dataset("sochastic/ChemPro", "mcq", split="difficult") |
| print(mcq[0]["question"]) |
| print(mcq[0]["choices"]) |
| print(mcq[0]["answer"], mcq[0]["answer_text"]) |
| |
| # All tiers at once |
| every_tier = load_dataset("sochastic/ChemPro", "mcq") |
| |
| # Numerical problems |
| num = load_dataset("sochastic/ChemPro", "numerical", split="easy") |
| print(num[0]["question"], num[0]["answer_value"], num[0]["answer_unit"]) |
| ``` |
|
|
| To filter by subfield: |
|
|
| ```python |
| organic = mcq.filter(lambda r: "Organic-Chemistry" in r["attributes"]) |
| ``` |
|
|
| ## Schema |
|
|
| Both configs share these fields: |
|
|
| | Field | Type | Description | |
| |---|---|---| |
| | `id` | string | A stable identifier, for example `chempro-difficult-mcq-01504`. You can cite it. | |
| | `source_id` | int32 | The index of the question in its source tier and question type | |
| | `tier` | string | The tier symbol used in the paper: `CP_E`, `CP_M`, `CP_C` or `CP_D`. The split name gives the readable form. | |
| | `source` | string | `web`, `ncert_grade_9_10`, `ncert_grade_11_12` or `jee_mains_2020_2024` | |
| | `question_type` | string | `mcq` or `numerical` | |
| | `question` | string | The question stem. For MCQ items, the options are in `choices`. | |
| | `attributes` | list[string] | One or more of the four subfields above | |
|
|
| The `mcq` config adds these fields: |
|
|
| | Field | Type | Description | |
| |---|---|---| |
| | `choices` | list[string] | Exactly four options, in the order A to D | |
| | `answer` | string | The letter of the correct option, `A` to `D` | |
| | `answer_index` | int32 | The zero-based index of the correct option in `choices` | |
| | `answer_text` | string | The text of the correct option. It always equals `choices[answer_index]`. | |
|
|
| The `numerical` config adds these fields: |
|
|
| | Field | Type | Description | |
| |---|---|---| |
| | `answer` | string | The answer as written. It includes the unit when the source gave one. | |
| | `answer_value` | float64 | The numeric value taken from `answer`. Use it for tolerance-based scoring. | |
| | `answer_unit` | string | The unit taken from `answer`. It is null when the answer has no unit. | |
|
|
| ### Examples |
|
|
| An MCQ item: |
|
|
| ```json |
| { |
| "id": "chempro-difficult-mcq-00001", |
| "tier": "CP_D", |
| "source": "jee_mains_2020_2024", |
| "question_type": "mcq", |
| "question": "The five successive ionization enthalpies of an element are ...", |
| "choices": ["2", "4", "3", "5"], |
| "answer": "C", |
| "answer_index": 2, |
| "answer_text": "3", |
| "attributes": ["Inorganic-Chemistry"] |
| } |
| ``` |
|
|
| A numerical item: |
|
|
| ```json |
| { |
| "id": "chempro-easy-numerical-00001", |
| "tier": "CP_E", |
| "question_type": "numerical", |
| "question": "Calculate the molar mass of $$ CH_3COOH $$.", |
| "answer": "60.05 g/mol", |
| "answer_value": 60.05, |
| "answer_unit": "g/mol", |
| "attributes": ["Physical-Chemistry"] |
| } |
| ``` |
|
|
| Chemical notation stays in LaTeX between `$$` delimiters. Remove the delimiters |
| or render them, as your evaluation requires. |
|
|
| ## Evaluation notes |
|
|
| - **MCQ.** Score against `answer`, which gives the letter, or against |
| `answer_text`. The options are an ordered list, so you can shuffle them to |
| control position bias. If you shuffle them, set `answer_index` again. |
| - **Numerical.** `answer_value` supports exact-match scoring and tolerance-based |
| scoring. Tolerance-based scoring tells you more, because many items round at |
| intermediate steps. The unit is a separate field, so a correct value does not |
| lose marks for its format. |
| - Report the result for each tier. A single average hides the fall in accuracy |
| that this benchmark shows. |
|
|
| ## Construction |
|
|
| We took the questions from the sources above. We put them into one text format. |
| Three passes then verified them: |
|
|
| 1. A source check confirmed the origin of each question. |
| 2. An expert review confirmed that each question is correct and has one meaning. |
| 3. Automated checks looked for format errors and duplicates. |
|
|
| GPT-4o applied the subfield `attributes`. Human annotators did not. Use these |
| labels to slice the data. Do not use them as a gold-standard taxonomy. |
|
|
| Each tier holds its full number of questions. Every question matches the |
| difficulty level and the subject mix of its tier. |
|
|
| ## Limitations |
|
|
| - **Possible exposure.** The `difficult` tier comes from the JEE Mains papers of |
| 2020 to 2024. These papers are public and may appear in pretraining data. The |
| paper estimates roughly 8% possible exposure. It used four probes: prefix |
| completion, paraphrase detection, content modification, and reverse |
| engineering. A comparison with other benchmarks found minimal overlap with |
| existing chemistry datasets. |
| - **Text only.** Some questions first used chemical structure diagrams. These |
| questions now use text. We rewrote or removed the items that text could not |
| show correctly. The benchmark therefore under-represents structure |
| recognition. |
| - **Curricular scope.** The difficulty comes from the Indian secondary and |
| competitive examination system. It applies well to general chemistry ability. |
| Do not read the tier names as universal difficulty labels. |
| - **Answer keys.** Every item passes automated structural validation. We |
| cross-checked the answer keys wherever two near-identical questions disagreed. |
| This method cannot find an error that both copies of a question share. It also |
| cannot find an error in an item that has no near-duplicate. Assume a small |
| number of remaining errors, as in any benchmark of this size. |
| - **Language.** The dataset is in English only. |
| - **No training split.** ChemPro is an evaluation benchmark. The tiers are splits |
| for convenience. No split is intended for training. |
|
|
| ## Licensing and provenance |
|
|
| ChemPro uses the [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) |
| licence. You can share and adapt the dataset for non-commercial purposes. You |
| must give attribution. |
|
|
| The `medium` and `challenging` tiers come from NCERT curricular material. The |
| `difficult` tier comes from JEE Mains examination papers. The National Testing |
| Agency administers those examinations. We adapted the questions. We did not copy |
| them word for word. The non-commercial term follows from these sources. If you |
| plan to use the dataset for more than non-commercial research, check the status |
| of the source material first. |
|
|
| ## Citation |
|
|
| If you use ChemPro, please cite the journal article: |
|
|
| ```bibtex |
| @article{baranwal2026chempro, |
| title = {ChemPro: A progressive chemistry benchmark for Large Language Models}, |
| author = {Baranwal, Aaditya and Vyas, Shruti}, |
| journal = {Artificial Intelligence Chemistry}, |
| volume = {4}, |
| number = {1}, |
| pages = {100118}, |
| year = {2026}, |
| issn = {2949-7477}, |
| doi = {10.1016/j.aichem.2026.100118}, |
| url = {https://www.sciencedirect.com/science/article/pii/S2949747726000126} |
| } |
| ``` |
|
|
| The preprint: |
|
|
| ```bibtex |
| @article{baranwal2026chempro_arxiv, |
| title = {ChemPro: A Progressive Chemistry Benchmark for Large Language Models}, |
| author = {Baranwal, Aaditya and Vyas, Shruti}, |
| journal = {arXiv preprint arXiv:2602.03108}, |
| year = {2026}, |
| doi = {10.48550/arXiv.2602.03108}, |
| url = {https://arxiv.org/abs/2602.03108} |
| } |
| ``` |
|
|
| - **Paper:** https://www.sciencedirect.com/science/article/pii/S2949747726000126 |
| - **Preprint:** https://arxiv.org/abs/2602.03108 |
|
|
| ## Contact |
|
|
| Aaditya Baranwal, University of Central Florida. Email: aaditya.baranwal@ucf.edu |
|
|
