SciInstruct: a Self-Reflective Instruction Annotated Dataset for Training Scientific Language Models
Paper · 2024 · Dan Zhang, Ziniu Hu, Sining Zhoubian et al.
Summary
Even strong LLMs often fail college-level science problems. These problems demand the right physical concepts, correct symbolic derivations and accurate arithmetic. Most legally available science data has only question–answer pairs with no worked reasoning. The authors close this gap with SciInstruct, an instruction-tuning corpus covering physics, chemistry, mathematics and Lean formal proofs.
To create the missing reasoning traces, they use a self-reflective annotation pipeline. An LLM first writes a step-by-step solution. When its final answer is wrong, the model diagnoses the error and tries again, in a later round with the reference answer supplied. A trained instruction-quality classifier then discards weak traces, leaving 254,051 verified samples.
Fine-tuning ChatGLM3 (6B, 32B) produces the SciGLM models, and Llama3-8B-Instruct and Mistral-7B: MetaMath were also tuned. All gain on scientific and math benchmarks while keeping their general language and coding ability. The work, presented at NeurIPS 2024 (Datasets and Benchmarks), shows that AI-generated, self-corrected reasoning can stand in for scarce expert annotations. Code and data are public.
Key points
- SciInstruct holds 254,051 filtered instructions drawn from textbooks, teaching materials and problem sets. It spans physics, chemistry, math and Lean theorem proofs.
- The pipeline has three stages: CoT generation, critique-and-revise for wrong answers, and filtering with an instruction-quality classifier.
- SciGLM raises average scientific-benchmark accuracy by 4.87% at 6B and 2.67% at 32B. It beats same-size models such as Galactica (science) and MAmmoTH (math).
- In data-scaling tests, the first 10% of the data already helps. Gains then level off until the share passes 50%, which the authors link to harder skills such as equation deduction.
- In Pass@K analysis, SciGLM (32B) roughly matches GPT-4 at K=25 on SciBench and K=3 on SciEval. Fine-tuning does not reduce sample diversity.
Fields, topics and concepts
📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 19,503 worksArtificial Intelligence · 18,805 worksArts and Humanities · 17,959 worksLanguage and Linguistics · 17,789 worksComputer Vision and Pattern Recognition · 3,621 works
🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 4,145 works
Cite
Cite as Dan Zhang, Ziniu Hu, Sining Zhoubian, Zhengxiao Du, Kaiyu Yang, Zihan Wang, Yisong Yue, Yuxiao Dong and Jie Tang. 2024. SciInstruct: a Self-Reflective Instruction Annotated Dataset for Training Scientific Language Models. arXiv:2401.07950.
@misc{zhang2024sciinstruct,
title = {{SciInstruct: a Self-Reflective Instruction Annotated Dataset for Training Scientific Language Models}},
author = {Dan Zhang, Ziniu Hu, Sining Zhoubian, Zhengxiao Du, Kaiyu Yang, Zihan Wang, Yisong Yue, Yuxiao Dong and Jie Tang},
year = {2024},
doi = {10.48550/arxiv.2401.07950},
eprint = {2401.07950},
archivePrefix = {arXiv},
url = {https://doi.org/10.48550/arxiv.2401.07950}
}References
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