LLM-based MOFs Synthesis Condition Extraction using Few-Shot Demonstrations
<|reference_start|>LLM-based MOFs Synthesis Condition Extraction using Few-Shot Demonstrations: The extraction of Metal-Organic Frameworks (MOFs) synthesis conditions from literature text has been challenging but crucial for the logical design of new MOFs with desirable functionality. The recent advent of large language models (LLMs) provides disruptively new solution to this long-standing problem and latest researches have reported over 90% F1 in extracting correct conditions from MOFs literature. We argue in this paper that most existing synthesis extraction practices with LLMs stay with the primitive zero-shot learning, which could lead to downgraded extraction and application performance due to the lack of specialized knowledge. This work pioneers and optimizes the few-shot in-context learning paradigm for LLM extraction of material synthesis conditions. First, we propose a human-AI joint data curation process to secure high-quality ground-truth demonstrations for few-shot learning. Second, we apply a BM25 algorithm based on the retrieval-augmented generation (RAG) technique to adaptively select few-shot demonstrations for each MOF's extraction. Over a dataset randomly sampled from 84,898 well-defined MOFs, the proposed few-shot method achieves much higher average F1 performance (0.93 vs. 0.81, +14.8%) than the native zero-shot LLM using the same GPT-4 model, under fully automatic evaluation that are more objective than the previous human evaluation. The proposed method is further validated through real-world material experiments: compared with the baseline zero-shot LLM, the proposed few-shot approach increases the MOFs structural inference performance (R^2) by 29.4% in average.<|reference_end|>
@article{shi2024llm-based,
    title={LLM-based MOFs Synthesis Condition Extraction using Few-Shot
  Demonstrations},
    author={Lei Shi, Zhimeng Liu, Yi Yang, Weize Wu, Yuyang Zhang, Hongbo Zhang,
  Jing Lin, Siyu Wu, Zihan Chen, Ruiming Li, Nan Wang, Zipeng Liu, Huobin Tan,
  Hongyi Gao, Yue Zhang, Ge Wang},
    journal={arXiv preprint arXiv:2408.04665},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.04665},
    primaryClass={cs.CL cs.AI}
}