# Generative LLMs in Organic Chemistry: Transforming
## 7 Discussion
\begin{table}
\begin{tabular}{c c} \hline \hline
**True** & **Prediction** \\ \hline \multicolumn{3}{c}{**Technically correct**} \\ \hline MAKESOLUTION with \$R25 and \$R15 and HOBT and DCC & MAKESOLUTION with \$R25 and DCC and HBT and \$R15 and THF; ADD SLN; STIR for 16 h at 25 C; CONCENTRATE; PARTITION with Ethyl acetate and K\({}_{2}\)CO\({}_{3}\); COLLECTLAYER organic; DRYSOLUTION over Na\({}_{2}\)SO\({}_{4}\); FILTER keep filtrate; CONCENTRATE; PURIFY; YIELD \$P15. & MAKESOLUTION with \$R15 and \$R25; ADD SLN; ADD \\ STIR for 24 h at 75 C; CONCENTRATE; ADD Ethyl acetate; ADD NaHCO\({}_{3}\); ADD water; DRYSOLUTION over MgSO\({}_{4}\); FILTER keep filtrate; CONCENTRATE; DRYSOLITO; WASH with brine; DRYSOLUTION over MgSO\({}_{4}\); FILTER keep filtrate; CONCENTRATE; YIELD \$P15. & MAKESOLUTION with \$R15 and \$R25; ADD SLN; ADD \\ STIR for 24 h at 75 C; CONCENTRATE; ADD Ethyl acetate; ADD NaHCO\({}_{3}\); ADD water; DRYSOLUTION over MgSO\({}_{4}\); & H\({}_{2}\)SO\({}_{4}\); STIR for 16 h at 85 C; CONCENTRATE; PARTITION with Ethyl acetate and NaHCO\({}_{3}\); COLLECTLAYER organic; WASH with brine; DRYSOLUTION over MgSO\({}_{4}\); FILTER keep filtrate; CONCENTRATE; YIELD \$P15. & MAKESOLUTION with \$R15 and \$R25; ADD SLN; ADD \\ STIR for 24 h at 75 C; CONCENTRATE; ADD Ethyl acetate; ADD NaHCO\({}_{3}\); ADD water; DRYSOLUTION over MgSO\({}_{4}\); & H\({}_{2}\)SO\({}_{4}\); STIR for 16 h at 85 C; CONCENTRATE; PARTITION with Ethyl acetate and NaHCO\({}_{3}\); COLLECTLAYER organic; WASH with brine; DRYSOLUTION over MgSO\({}_{4}\); FILTER keep filtrate; CONCENTRATE; YIELD \$P15. Interestingly, the TF-IDF+kNN algorithm is not the worst, i.e., it was able to outperform generative transformer babbage-002. The highest BLEU score of 46.69 was achieved with the Levenshtein distance metric using the largest training dataset. Its underlying principle of using similar reactions to predict procedures seems aligned with the task at hand. While TF-IDF+kNN is a relatively simple algorithm, its performance, which closely rivals that of some transformer-based methods in this domain, is truly remarkable. The evaluated methods have the potential of both traditional and state-of-the-art models in processing and understanding chemical reactions. The FLAN-T5-base model stands out among deep learning models with its superior BLEU score, which can be attributed to its sequence-to-sequence architecture. However, the second-best, OpenAI's GPT-3.5-turbo, while not outperforming FLAN-T5-base, also demonstrates its adaptability. One of the key takeaways from the results is that fine-tuned OpenAI models can be outperformed by a significantly smaller model having no knowledge about chemistry, which can be fine-tuned on a single workstation. A feature of the FLAN-T5-base model is its autonomous nature, serving as an independent, self-contained solution that does not require reliance on third-party services. Furthermore, this model is accessible free of charge, enhancing its appeal for researchers and institutions looking to perform research without incurring additional costs. This aspect reinforces the model's value as a practical tool for scientific inquiry where budget constraints are a consideration. Our results demonstrate that models for specific tasks need to be tested on many different types and sizes of model to discern the top performers. Furthermore, the positive outcomes provide a strong impulse for further exploration in this area, including the utilization of larger datasets that encompass a broader range of reaction types and the exploration of various models. Moreover, there exist numerous other large language models, and new ones continue to emerge regularly. In addition, specific findings, such as the superior performance of the FLAN-T5 model (despite its initial limited chemistry knowledge) compared to GPT-3.5-turbo, raise further questions. There might be a need for a model with a clear separation between chemistry-specific input and structured yet natural language text generation in the output.