Recently, there are many works on smaller models that achieve inspiring dialog abilities, which makes people imagine if smaller models can have comparable performance to large models like GPT-3.5. Generally, language models have multi-dimensional abilities, which makes them hard to compare. Finding the correct metric is crucial for developing strong language models. At the current stage, the community is eager to know what are the key differentiators that mark the potential of strong language models.

In GPT-4 release blog, the authors write: “In a casual conversation, the distinction between GPT-3.5 and GPT-4 can be subtle. The difference comes out when *the complexity of the task reaches a sufficient threshold*”. This means that complex tasks are likely to be the key differentiators for large v.s. small language models.

More importantly, complex reasoning opens up opportunities for building a large spectrum of applications upon language models, effectively making language models the next-generation computation platform/ operating system. This has the potential to substantially change the way humans interact with computers and reshape the whole computational ecosystem.

In this post, we take a close look at methods toward models of strong complex reasoning capabilities.