# Generative LLMs in Organic Chemistry: Transforming
## 7 Discussion  
Upon closer observation, one can note the correlation between the data size and performance in all of the tested approaches. The increased performance observed from training on from 50 to 1000 data points indicates that all of the tested deep learning models, particularly FLAN-T5, benefit significantly from larger datasets. The only exception is davinci-002 (between size 50 and size 150); however, the difference is minimal. Such an observation suggests that larger datasets are necessary for even better results. The FLAN-T5 model benefits the most from the increased size of the training dataset (a difference in score of 12.48 between size 50 (39.34) and 1000 (51.82)), while the GPT-3.5-turbo saw the least amount of improvement (6.45) between size 50 (41.54) and 1000 (47.99). This also suggests that the larger models need even more data to benefit from the size of the training dataset. Considering the nature of chemistry, each input is often unique, because different products are being synthesized; therefore, models must deal with a significant variety of text. It has been shown that few-shot learning can be applied to a variety of tasks; however, contrasting our results with the English language, where recurrent phrases can assist in semantic extraction, the challenges in chemistry text processing are evident [63]. Figure 1: BLEU scores of the testing dataset. Therefore, any discussion of results in the following section will consider only training on the largest dataset. Compared to the random and majority baseline scores (random--36.56, majority--24.16), all of the tested methods with all sizes of training datasets demonstrate a reasonable performance and predictive power. Looking at Table 3, the FLAN-T5 model has emerged as the best among all of the tested approaches, achieving the top BLEU score of 51.82, which can be considered as high-quality translation. One can attribute this to the sequence-to-sequence nature of FLAN-T5, which makes it well-suited for translating chemical reactions from reactants and products to procedures. Despite this, the OpenAI models (particularly GPT-3.5-turbo) underperform compared to FLAN-T5 but also deliver promising results. With a BLEU score only marginally less than FLAN-T5 (47.99), GPT-3.5-turbo showcases the versatility of transformer architectures, which, although initially designed for language processing, can learn to understand the intrinsic structure and relationships in molecular notation. Of course, structured, but more natural, language in the output makes the task a little easier, as models consider all of their context (including the generated content), but the input remains complicated. Paradoxically, despite starting from a lower position (i.e., lacking the ability to generate any procedures, even very rudimentary ones, and not having demonstrated proficiency in answering questions of a chemical nature), the FLAN-T5-base model (with 250 M parameters) was able to surpass the GPT-3.5-turbo (154 billion parameters) (which was capable of generating very basic and general procedures for synthesis reactions). GPT-3.5-turbo possessed extensive language knowledge, which may have overlapped with its understanding of chemical knowledge, potentially leading to ambiguity. In contrast, FLAN-T5 gathered all of the necessary information primarily from the training dataset. This difference may help explain why FLAN-T5 outperformed GPT-3.5-turbo. When comparing the GPT models with each other, GPT-3.5-turbo and davinci-002 (with 6 billion parameters) achieved very similar results, especially with the largest dataset, and both outperformed babbage-002 (125 million parameters), which is the smallest of the three. There seems to be a clear correlation between a GPT model's complexity and its ability to adapt effectively to novel problems it has never encountered before.