README.md

---
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