Inmany cases, increasing model capacity beyond the memory limit of a single acceleratorhas required developing special algorithms orinfrastructure. These solutions are often architecture-specific and do not transfer to other tasks.

Non-autoregressive Transformers (NA Ts) are recently applied in direct speech-to-speech translation systems, which convert speech across different languages without intermediate text data.

Generated text may contain offensive or toxic language, contain significant repetition, orbeofadifferent sentiment than desired by the user. We consider thetaskofunlearningthese misalignments byfine-tuning thelanguage model on signals of whatnot to do.

Self-supervised large language models have demonstrated the ability to perform various tasks via in-context learning, but little is known about where the model locates the task with respect to prompt instructions and demonstration examples. In this work, we attempt to characterize the region where large language models transition from recognizing the task to performing the task.

Inthesenetworks,attentionalignsencoderand decoder states and isoften used for visualizing network behavior.