GPT-4.5, like its predecessor GPT-4, faces several limitations in chemical research. These limitations are crucial to understanding the potential and challenges of using large language models in this field.
1. Foundational Knowledge and Specialized Tasks
- Knowledge Base: GPT-4.5, similar to GPT-4, has a strong foundation in textbook-level chemistry, including physical and organic chemistry principles. However, it may not have access to the latest research articles due to copyright restrictions and training data limitations[1].- Specialized Tasks: In tasks such as reaction prediction and retrosynthesis, GPT models generally perform worse than specialized machine learning models trained on specific chemical datasets[8]. This indicates that while GPT-4.5 can provide general insights, it may not be as effective in complex, specialized chemical tasks.
2. Data Analysis and Visualization
- GPT-4.5, like other large language models, struggles with presenting complex data analysis results in a clear and concise manner, including the use of visualizations, tables, and graphs[2]. This limitation makes it less effective for researchers who need to communicate complex findings effectively.- It lacks the ability to deeply understand statistical methodologies, identify biases, and handle missing data, which are critical skills in data analysis[2].
3. Literature Review and Source Evaluation
- GPT-4.5 cannot scrutinize academic sources comprehensively, which is essential for conducting thorough literature reviews. It may struggle to differentiate between high-quality and less reliable sources, potentially leading to inaccuracies in generated reviews[2].- The model's inability to provide accurate lists of referenced articles is another limitation when writing scientific papers[6].
4. Chemical Synthesis and Experimental Procedures
- GPT-4.5, similar to GPT-4, may not provide accurate or detailed experimental procedures for chemical synthesis due to safety concerns and limitations in understanding complex chemical reactions[1]. It may propose chemically incorrect synthesis routes or misunderstand reaction mechanisms.- The model's inability to accurately estimate molecular structures from compound names can lead to incorrect reasoning in cheminformatics tasks[1].
5. Integration with Specialized Tools
- For tasks involving chemical reactions, GPT-4.5 may benefit from integration with specialized chemical software or programming languages to enhance its performance[1]. This integration could help overcome some of its limitations in handling complex chemical data and reactions.6. Ethical and Safety Considerations
- GPT-4.5, as noted in the system card, poses a medium risk in chemical and biological threat creation due to its ability to assist in operational planning for known threats[3]. However, this risk is mitigated by the assumption that users already possess significant domain expertise.In summary, while GPT-4.5 offers promising capabilities in chemical research, such as foundational knowledge and data analysis, it faces significant limitations in specialized tasks, data visualization, literature review, and safety considerations. Its effectiveness is enhanced when used in conjunction with specialized tools and domain expertise.
Citations:
[1] https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/647d305dbe16ad5c577b6627/original/prompt-engineering-of-gpt-4-for-chemical-research-what-can-cannot-be-done.pdf
[2] https://www.enago.com/academy/chatgpt-cannot-do-for-researchers-2/
[3] https://cdn.openai.com/gpt-4-5-system-card.pdf
[4] https://community.openai.com/t/what-are-the-limitations-of-gpt-4-in-analyzing-pdf-text/534760
[5] https://www.tandfonline.com/doi/full/10.1080/27660400.2023.2260300
[6] https://pmc.ncbi.nlm.nih.gov/articles/PMC11184879/
[7] https://www.reddit.com/r/ChatGPT/comments/1iznoek/gpt45_system_card_mmlu_896/
[8] https://arxiv.org/html/2305.18365v3