Inspired by the neural mechanisms that may contribute to the brain's associative power, specifically the cortical modularization and hippocampal pattern completion, here we propose a self-supervised controllable generation (SCG) framework.

In a single-agent setting, IL was shown to be done efficiently via an inverse soft-Q learning process.

Recall that ˆmc(u) is exactly the listener's decoder in the IB framework (see Section 3.1.1). Therefore, anyother decoder would lend an upper bound on the informativeness loss term. Notice that under our assumptions,ˆmc is a Gaussian mixture, whereas the speaker's beliefs are simply Gaussian. All the systems with the samek form an equivalence class and the canonical system within each class is the one with minimalk. These canonical systems are the natural one to prefer, because they can attain the optimum for a given complexity with aminimal codebook.

Here, we integrate these two approaches by trading off utility, informativeness, and complexity in EC.

Notably,chainofthought(CoT)prompting, a recent technique for eliciting complex multi-step reasoning through step-bystep answer examples, achieved the state-of-the-art performances in arithmetics and symbolic reasoning, difficultsystem-2 tasks that do not follow the standard scaling laws for LLMs. While these successes are often attributed to LLMs' ability forfew-shot learning, weshowthatLLMs aredecentzero-shotreasoners by simply adding "Let's think step by step" before each answer.