AI Technology & Industry Review
In the new paper Vec2text With Round-Trip Translations, Google Brain researchers explore large language models’ capabilities for generating arbitrary natural language text from inputs of fixed-size vectors — a vec2text setting — and propose a simple data augmentation approach based on round-trip translations to improve vec2text model performance.
In the new paper PEER: A Collaborative Language Model, a research team from Meta AI, Carnegie Mellon University, PSL University, and University College London presents PEER, a collaborative language model that performs a humanlike writing process — composing drafts, adding suggestions, proposing edits and providing explanations for its actions.
A Stanford research team proposes Time Control (TC), a language model that implicitly plans via a latent stochastic process and generates texts consistent with this latent plan to improve performance on long text generation.
In the paper ReGen: Reinforcement Learning for Text and Knowledge Base Generation Using Pretrained Language Models, IBM researchers present ReGen, a bidirectional generation of text and graph that leverages reinforcement learning to push the performance of text-to-graph and graph-to-text generation tasks to a higher level.
Researchers have proposed a novel self-adversarial learning (SAL) paradigm for improving GANs’ performance in text generation.
Petuum and Carnegie Mellon University (CMU) researchers have introduced Texar, an open-source general purpose text generation toolkit that can help boost R&D in fast model prototyping and experimentation.