ChemPro v1.0 - 4,100 chemistry questions across four difficulty tiers

CITATION.cff

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+cff-version: 1.2.0
+message: "If you use ChemPro, please cite the journal article below."
+title: "ChemPro: A Progressive Chemistry Benchmark for Large Language Models"
+abstract: >-
+  ChemPro is a progressive benchmark of chemistry question-answer pairs spanning
+  four curriculum-aligned difficulty tiers, covering multiple-choice and numerical
+  questions across biochemistry, inorganic, organic and physical chemistry.
+type: dataset
+license: CC-BY-NC-4.0
+authors:
+  - family-names: Baranwal
+    given-names: Aaditya
+    affiliation: University of Central Florida
+    email: aaditya.baranwal@ucf.edu
+  - family-names: Vyas
+    given-names: Shruti
+    affiliation: University of Central Florida
+keywords:
+  - chemistry
+  - benchmark
+  - large language models
+  - evaluation
+  - question answering
+preferred-citation:
+  type: article
+  title: "ChemPro: A progressive chemistry benchmark for Large Language Models"
+  authors:
+    - family-names: Baranwal
+      given-names: Aaditya
+    - family-names: Vyas
+      given-names: Shruti
+  journal: "Artificial Intelligence Chemistry"
+  volume: 4
+  issue: 1
+  start: 100118
+  year: 2026
+  issn: "2949-7477"
+  doi: "10.1016/j.aichem.2026.100118"
+  url: "https://www.sciencedirect.com/science/article/pii/S2949747726000126"
+references:
+  - type: article
+    title: "ChemPro: A Progressive Chemistry Benchmark for Large Language Models"
+    authors:
+      - family-names: Baranwal
+        given-names: Aaditya
+      - family-names: Vyas
+        given-names: Shruti
+    year: 2026
+    doi: "10.48550/arXiv.2602.03108"
+    url: "https://arxiv.org/abs/2602.03108"
+    notes: "arXiv preprint"

LICENSE

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README.md

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+---
+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

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