Roll the dice & look before you leap: Going beyond the creative limits
  of next-token prediction
Authors: name: Vaishnavh Nagarajan, name: Chen Henry Wu, name: Charles Ding, name: Aditi Raghunathan

We design a suite of minimal algorithmic tasks that are a loose abstraction
of open-ended real-world tasks. This allows us to cleanly and controllably
quantify the creative limits of the present-day language model. Much like
real-world tasks that require a creative, far-sighted leap of thought, our
tasks require an implicit, open-ended stochastic planning step that either (a)
discovers new connections in an abstract knowledge graph (like in wordplay,
drawing analogies, or research) or (b) constructs new patterns (like in
designing math problems or new proteins). In these tasks, we empirically and
conceptually argue how next-token learning is myopic and memorizes excessively;
comparatively, multi-token approaches, namely teacherless training and
diffusion models, excel in producing diverse and original output. Secondly, in
our tasks, we find that to elicit randomness from the Transformer without
hurting coherence, it is better to inject noise right at the input layer (via a
method we dub hash-conditioning) rather than defer to temperature sampling from
the output layer. Thus, our work offers a principled, minimal test-bed for
analyzing open-ended creative skills, and offers new arguments for going beyond
next-token learning and softmax-based sampling. We make part of the code
available under https://github.com/chenwu98/algorithmic-creativity