For the randomly selected 50 DOIs, we require each model to generate 20 Q&A pairs for each DOI. Thus, the total size of the database is approximately 6,000 Q&A pairs (50 DOIs × 20 Q&A pairs × 3 models(GPT-4, Claude, and Gemini) × 2 tasks(single-hop and multi-hop)). Every Q&A pair is evaluated by subject matter experts. It is worth mentioning that the evaluation results of GPT-4 have already been published in the RetChemQA dataset program. For the single-hop task, we evaluated the three LLMs -GPT-4, Claude, and Gemini -based on Accuracy (to assess the quality of the answers), Groundedness (to evaluate whether the questions were derived from the paper), Precision (to determine whether questions were derived from the main text and answered correctly), and Hallucination Capture Rate (to assess the self-correction ability of LLMs). As shown in Figure , both Claude and Gemini achieved scores exceeding 99% in accuracy, groundedness and precision. And the hallucination capture rates of both were 50.0%. In contrast, GPT-4's accuracy was 90.7%, while groundedness was slightly higher at 92.6%, precision was 89.8%, and hallucination capture rate was only 11.4%. These results indicate that Claude and Gemini are highly reliable for generating single-hop Q&A datasets, making them suitable as benchmarks for future studies. The consistently high performance of these two models suggests a strong ability to extract relevant information accurately while maintaining a high standard of quality across diverse question types and categories.