Large language models (LLMs) have an interesting feature that is often overlooked: they provide “live” answers to prompts. When prompted, these models start talking and continue until they have finished their response. It’s like having a conversation with a person who improvises their answer sentence by sentence.
This characteristic of LLMs explains why they can be frustrating at times. Within the same paragraph, the model may contradict itself, saying one thing and then immediately stating the opposite. This happens because the model is essentially “reasoning aloud” and adjusting its impressions on the fly. As a result, these AI systems require significant guidance to engage in complex reasoning.
To address this issue, one approach called chain-of-thought prompting has been developed. With this technique, large language models are asked to think out loud about a problem and provide an answer only after laying out all of their reasoning step by step.
OpenAI’s latest model, o1 (nicknamed Strawberry), is the first major LLM release that incorporates this “think, then answer” approach. According to OpenAI’s reports, o1 performs similarly to PhD students on challenging tasks in physics, chemistry, biology, math, and coding.
However, while this improvement in thinking abilities is impressive from an AI standpoint, it also raises concerns about potential risks associated with such advanced capabilities. OpenAI tests its models for dangerous applications like chemical and biological weapons before release.
The development of more intelligent language models brings both benefits and risks as AI becomes a dual-use technology with wide-ranging applications across various fields.
Evaluating AI systems can be challenging due to the lack of scientific measures for assessing their capabilities accurately. Selective testing can lead to biased judgments about their performance instead of considering the bigger picture.
Despite these challenges and limitations in current LLMs’ economic applications due to reliability issues inherent in these models’ design principles; incremental improvements continue pushing them closer towards becoming essential tools rather than mere party tricks.
OpenAI’s o1 release demonstrates a commitment to addressing policy implications by collaborating with external organizations for evaluation purposes—a crucial aspect as AI continues advancing rapidly.
In conclusion: while there may not be a single solution that solves all limitations of large language models at once; gradual improvements over time will likely erode those limitations incrementally—similarly how AI has progressed thus far.
