SciAssess: A Benchmark for Evaluating Large Language Models in Scientific Literature Analysis

Version: 1.0.0

SciAssess is a comprehensive benchmark designed to evaluate the proficiency of Large Language Models (LLMs) in scientific literature analysis. It focuses on assessing LLMs' abilities in memorization, comprehension, and analysis within the context of scientific literature, covering a wide range of scientific fields such as general chemistry, organic materials, and alloy materials. SciAssess provides a rigorous and thorough assessment of LLMs, supporting the ongoing development of LLM applications in scientific literature analysis.

For more details, please refer to our paper: SciAssess: Benchmarking LLM Proficiency in Scientific Literature Analysis.

Domains and Tasks

Domain	Task	Ability	# Questions	Context	Question Type	Metric	Modality
Fundamental Science	MMLU (science)	L1	2,091		Multiple Choice	Accuracy	Text only
	CMMLU (science)	L1	1,700		Multiple Choice	Accuracy	Text only
	Xiezhi-Ch (science)	L1	2,882		Multiple Choice	Accuracy	Text only
	Xiezhi-En (science)	L1	2,882		Multiple Choice	Accuracy	Text only
Alloy Materials	Alloy Chart QA	L2	15	✔️	Multiple Choice	Accuracy	Chart
	Composition Extraction	L2	244	✔️	Table Extraction	Table Accuracy	Table
	Temperature Extraction	L2	207	✔️	Multiple Choice	Accuracy	Text only
	Sample Differentiation	L3	237	✔️	Multiple Choice	Accuracy	Text only
	Treatment Sequence	L3	102	✔️	True/False	Accuracy	Text only
Biomedicine	Biology Chart QA	L2	99	✔️	Multiple Choice	Accuracy	Chart
	Chemical Entities Recognition	L2	997		Text Extraction	Recall	Text only
	Disease Entities Recognition	L2	997		Text Extraction	Recall	Text only
	Compound Disease Recognition	L3	997		Text Extraction	Recall	Text only
	Gene Disease Function	L3	236		Text Extraction	Recall	Text only
	Gene Disease Regulation	L3	240		Text Extraction	Recall	Text only
Drug Discovery	Affinity Extraction	L2	40	✔️	Table Extraction	Table Accuracy	Mol., Table
	Drug Chart QA	L2	15	✔️	Multiple Choice	Accuracy	Chart
	Tag to Molecule	L2	50	✔️	Molecule Generation	Mol. Similarity	Mol.
	Markush to Molecule	L3	37		Molecule Generation	Mol. Similarity	Mol.
	Molecule in Document	L3	50	✔️	True/False	Accuracy	Mol.
	Reaction QA	L3	95	✔️	Multiple Choice	Accuracy	Reaction
	Drug Target Identification	L3	40	✔️	Text Extraction	Recall	Text only
Organic Materials	Electrolyte Table QA	L2	100	✔️	Multiple Choice	Accuracy	Table
	OLED Property Extraction	L2	13	✔️	Table Extraction	Table Accuracy	Mol.,Table
	Polymer Chart QA	L2	15	✔️	Multiple Choice	Accuracy	Chart
	Polymer Composition QA	L2	109	✔️	Multiple Choice	Accuracy	Text only
	Polymer Property Extraction	L2	109	✔️	Table Extraction	Table Accuracy	Table
	Solubility Extraction	L2	100	✔️	Table Extraction	Table Accuracy	Table
	Reaction Mechanism QA	L3	22	✔️	Multiple Choice	Accuracy	Reaction

Performance

Table1: Overall Performance

Domain	Task	ICL	GPT-4o	GPT-4	GPT-3.5	Moonshot	Claude3	Doubao	Gemini	Llama3	DeepSeek	Qwen2	Command R+
Fundamental Science	MMLU (science)	0-shot	0.839	0.783	0.629	0.774	0.795	0.720	0.799	0.766	0.737	0.782	0.647
		3-shot	0.846	0.769	0.614	0.774	0.771	0.712	0.790	0.757	0.738	0.789	0.643
	CMMLU (science)	0-shot	0.785	0.644	0.438	0.723	0.643	0.841	0.731	0.651	0.769	0.870	0.448
		3-shot	0.785	0.646	0.432	0.728	0.631	0.833	0.736	0.658	0.768	0.867	0.455
	Xiezhi-Ch (science)	0-shot	0.736	0.724	0.696	0.734	0.731	0.720	0.716	0.731	0.748	0.746	0.683
		3-shot	0.736	0.708	0.690	0.732	0.706	0.706	0.723	0.736	0.726	0.745	0.672
	Xiezhi-En (science)	0-shot	0.701	0.683	0.644	0.677	0.673	0.667	0.652	0.687	0.685	0.692	0.634
		3-shot	0.699	0.670	0.641	0.679	0.658	0.650	0.654	0.683	0.665	0.697	0.632
Alloy Materials	Alloy Chart QA	0-shot	0.533	0.600	0.333	0.333	0.400	0.467	0.667	0.467	0.333	0.400	0.200
	Composition Extraction	0-shot	0.484	0.458	0.112	0.127	0.495	0.304	0.239	0.212	0.389	0.423	0.128
	Temperature Extraction	0-shot	0.884	0.855	0.729	0.889	0.865	0.700	0.841	0.604	0.754	0.797	0.546
	Sample Differentiation	0-shot	0.511	0.591	0.169	0.679	0.586	0.316	0.658	0.376	0.616	0.557	0.228
	Treatment Sequence	0-shot	0.745	0.725	0.461	0.755	0.745	0.745	0.696	0.539	0.686	0.657	0.588
Biomedicine	Biology Chart QA	0-shot	0.580	0.480	0.390	0.545	0.505	0.480	0.616	0.520	0.545	0.515	0.535
	Chemical Entities Recognition	0-shot	0.454	0.665	0.540	0.201	0.844	0.911	0.678	0.400	0.536	0.832	0.850
		3-shot	0.916	0.898	0.912	0.912	0.898	0.900	0.858	0.855	0.911	0.905	0.871
	Disease Entities Recognition	0-shot	0.279	0.765	0.153	0.000	0.653	0.675	0.437	0.526	0.331	0.722	0.258
		3-shot	0.822	0.849	0.879	0.785	0.782	0.811	0.807	0.787	0.825	0.826	0.647
	Compound Disease Recognition	0-shot	0.755	0.786	0.733	0.770	0.788	0.771	0.733	0.794	0.757	0.794	0.764
		3-shot	0.743	0.750	0.715	0.773	0.763	0.719	0.719	0.785	0.716	0.753	0.715
	Gene Disease Function	0-shot	0.931	0.974	0.864	0.771	0.944	0.779	0.954	0.996	0.819	0.930	0.884
		3-shot	0.945	0.927	0.896	0.845	0.931	0.772	0.868	0.876	0.830	0.814	0.888
	Gene Disease Regulation	0-shot	0.949	0.914	0.832	0.944	0.939	0.910	0.856	0.971	0.952	0.963	0.936
		3-shot	0.939	0.926	0.917	0.957	0.951	0.912	0.886	0.958	0.943	0.953	0.936
Drug Discovery	Affinity Extraction	0-shot	0.072	0.042	0.025	0.040	0.097	0.050	0.040	0.064	0.017	0.075	0.043
	Drug Chart QA	0-shot	0.333	0.400	0.067	0.400	0.200	0.533	0.533	0.400	0.400	0.400	0.533
	Tag to Molecule	0-shot	0.040	0.022	0.000	0.016	0.035	0.094	0.169	0.034	0.014	0.000	0.031
	Markush to Molecule	0-shot	0.634	0.632	0.429	0.462	0.644	0.217	0.218	0.478	0.543	0.358	0.332
		3-shot	0.642	0.654	0.431	0.504	0.675	0.239	0.526	0.491	0.470	0.379	0.376
	Molecule in Document	0-shot	0.580	0.700	0.500	0.460	0.480	0.560	0.640	0.680	0.460	0.460	0.460
	Reaction QA	0-shot	0.705	0.674	0.442	0.253	0.663	0.442	0.305	0.611	0.368	0.442	0.316
	Drug Target Identification	0-shot	0.721	0.791	0.526	0.607	0.794	0.622	0.768	0.600	0.687	0.410	0.485
Organic Materials	Electrolyte Table QA	0-shot	0.940	0.790	0.370	0.670	0.870	0.710	0.880	0.460	0.720	0.620	0.450
	OLED Property Extraction	0-shot	0.336	0.406	0.201	0.037	0.477	0.259	0.093	0.263	0.292	0.392	0.234
	Polymer Chart QA	0-shot	0.800	0.667	0.400	0.800	0.467	0.867	0.800	0.867	0.733	0.933	0.800
	Polymer Composition QA	0-shot	0.945	0.945	0.853	0.844	0.881	0.927	0.927	0.734	0.881	0.936	0.679
	Polymer Property Extraction	0-shot	0.692	0.681	0.329	0.705	0.629	0.514	0.606	0.536	0.652	0.636	0.171
	Solubility Extraction	0-shot	0.479	0.440	0.410	0.363	0.426	0.371	0.397	0.399	0.432	0.400	0.351
	Reaction Mechanism QA	0-shot	0.545	0.636	0.455	0.545	0.455	0.636	0.727	0.500	0.545	0.591	0.591

Installation

To use SciAssess, first clone the repository:

git clone https://github.com/sci-assess/SciAssess.git
cd SciAssess

Install the required dependencies:

pip install -e .

Dataset

Due to copyright restrictions, we are unable to directly distribute the original PDF of the article. You will need to download the corresponding PDF according to the instructions in README and store it in SciAssess_library/pdfs.

All articles involved in this evaluation are listed in doi.txt. You need to download the corresponding PDFs according to the DOIs and store them in SciAssess_library/pdfs.

Each PDF should be named as doi.pdf, with '/' in the DOI replaced by '_', e.g., an article with DOI 10.1002/adfm.202008332 should be named as 10.1002_adfm.202008332.pdf and placed in SciAssess_library/pdfs.

Some articles' supporting information is also evaluated. These articles' DOIs are listed in si_doi.txt. You need to download the corresponding PDFs and store them in SciAssess_library/pdfs, named as doi_si.pdf.

Usage

If you want to evaluate your own model, you need to configure your model's registration information and implementation in sciassess/Registry/completion_fns and sciassess/Implement/completion_fns, respectively. See openai/evals:completion-fns.md for configuration instructions.

Note that most evaluations depend on the article PDFs, so you may need to process the input PDFs within your model's method. The PDF file path will be passed in the __call__ function through kwargs['file_name'], and you need to handle this parameter and process the PDF in the __call__ function. See openai_with_pdf.py for an example based on PyPDF and GPT.

After completing the model configuration, run the following command to evaluate your model:

bash run_sciassess.sh your_model_name

Replace your_model_name with the name of your model (default: gpt3.5).

Remember to export your OpenAI API key as an environment variable:

export OPENAI_API_KEY=your_openai_api_key

Version Information

0.9.0 (2024-03-17) Beta version first released

0.9.1 (2024-03-28) Fix critical bugs. Now the code is executable.

0.9.2 (2024-04-06) Optimize the metric of multiple choice questions.

0.9.3 (2024-04-07) Merge mmlu college chemistry and high school chemistry.

Remove abstract2title and research_question_extraction due to uncertainty of model grading.

1.0.0 (2024-04-08) Official version released

Contributing

We welcome contributions to the SciAssess benchmark. If you have any suggestions or improvements, please feel free to open an issue or create a pull request.

Citation

If you use SciAssess in your research, please cite our paper:

@misc{cai2024sciassess,
      title={SciAssess: Benchmarking LLM Proficiency in Scientific Literature Analysis}, 
      author={Hengxing Cai and Xiaochen Cai and Junhan Chang and Sihang Li and Lin Yao and Changxin Wang and Zhifeng Gao and Yongge Li and Mujie Lin and Shuwen Yang and Jiankun Wang and Yuqi Yin and Yaqi Li and Linfeng Zhang and Guolin Ke},
      year={2024},
      eprint={2403.01976},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}