However, there exist limitations in this approach because the LLM tended to hallucinate the missing information when presented with partially complete recipes. Although we carefully constructed the training data to ensure the LLM generates tokens used in the input text, we still encountered "weak hallucinations" where the model generated information not found in the input text. For instance, there were cases where a seed solution recipe was completely missing in the source text. we confirmed multiple cases where i) seeds were purchased and used as-is ii) the recipe is described without details (e.g. "seeds were synthesized using borohydride reduction") or iii) recipe is completely omitted. In such cases, we found that the model often confused the growth solution precursors and amounts with seed solution parameters, and this data was extracted as the seed solution recipe. Therefore, we had to manually filter cases and verify whether the information was indeed missing from the input text or not.