Ann: UNITH Investor Webinar rescheduled to Thursday 16 March 2023, page-23

5 Limitations
Despite its capabilities, GPT-4 has similar limitations as earlier GPT models. Most importantly, it still
is not fully reliable (it “hallucinates” facts and makes reasoning errors). Great care should be taken
when using language model outputs, particularly in high-stakes contexts, with the exact protocol
(such as human review, grounding with additional context, or avoiding high-stakes uses altogether)
matching the needs of specific applications. See our System Card for details.
7 Conclusion
We characterized GPT-4, a large multimodal model with human-level performance on certain difficult
professional and academic benchmarks. GPT-4 outperforms existing large language models on a
collection of NLP tasks, and exceeds the vast majority of reported state-of-the-art systems (which
often include task-specific fine-tuning). We find that improved capabilities, whilst usually measured
in English, can be demonstrated in many different languages. We highlighted how predictable scaling
allowed us to make accurate predictions on the loss and capabilities of GPT-4.
GPT-4 presents new risks due to increased capability, and we discussed some of the methods and
results taken to understand and improve its safety and alignment. Though there remains much work to
be done, GPT-4 represents a significant step towards broadly useful and safely deployed AI systems.