MAP's not dead yet: Uncovering true language model modes by conditioning
  away degeneracy
Davis Yoshida Kartik Goyal Kevin Gimpel
It has been widely observed that exact or approximate MAP (mode-seeking)
decoding from natural language generation (NLG) models consistently leads to
degenerate outputs (Stahlberg and Byrne, 2019, Holtzman et al., 2019). This has
generally been attributed to either a fundamental inadequacy of modes in models
or weaknesses in language modeling. Contrastingly in this work, we emphasize
that degenerate modes can even occur in the absence of any model error, due to
contamination of the training data. Specifically, we show that mixing even a
tiny amount of low-entropy noise with a population text distribution can cause
the data distribution's mode to become degenerate, implying that any models
trained on it will be as well. As the unconditional mode of NLG models will
often be degenerate, we therefore propose to apply MAP decoding to the model's
distribution conditional on avoiding specific degeneracies. Using exact-search,
we empirically verify that the length-conditional modes of machine translation
models and language models are indeed more fluent and topical than their
unconditional modes. For the first time, we also share many examples of exact
modal sequences from these models, and from several variants of the LLaMA-7B
model. Notably, the modes of the LLaMA models are still degenerate, showing
that improvements in modeling have not fixed this issue. Because of the cost of
exact mode finding algorithms, we develop an approximate mode finding approach,
ACBS, which finds sequences that are both high-likelihood and high-quality. We
apply this approach to LLaMA-7B, a model which was not trained for instruction
following, and find that we are able to elicit reasonable outputs without any
finetuning.