Yubing Wang | Searcharxiv

arXiv2026

A Unified Bogoliubov Approach to Primordial Gravitational Waves: From Inflation to Reheating

We present an effective numerical method that can be used to straightforwardly calculate the full spectrum of primordial gravitational waves produced during inflation and reheating. Our method is based on the Bogoliubov approach with several key improvements to overcome its shortcomings such as numerical errors at high frequencies and issues with tachyonic modes. We also present a few useful analytical examples from which one can gain crucial insights into the numerical errors. The improved method allows us to demonstrate that anharmonicity of inflaton oscillations can leave interesting fingerprints on the high-frequency part of the GW spectrum. Our numerical code is publicly available on GitHub https://github.com/xunjiexu/Unified-Bogoliubov.git.

hep-ph↗

arXiv2024

Encoder-Decoder Framework for Interactive Free Verses with Generation with Controllable High-Quality Rhyming

Composing poetry or lyrics involves several creative factors, but a challenging aspect of generation is the adherence to a more or less strict metric and rhyming pattern. To address this challenge specifically, previous work on the task has mainly focused on reverse language modeling, which brings the critical selection of each rhyming word to the forefront of each verse. On the other hand, reversing the word order requires that models be trained from scratch with this task-specific goal and cannot take advantage of transfer learning from a Pretrained Language Model (PLM). We propose a novel fine-tuning approach that prepends the rhyming word at the start of each lyric, which allows the critical rhyming decision to be made before the model commits to the content of the lyric (as during reverse language modeling), but maintains compatibility with the word order of regular PLMs as the lyric itself is still generated in left-to-right order. We conducted extensive experiments to compare this fine-tuning against the current state-of-the-art strategies for rhyming, finding that our approach generates more readable text and better rhyming capabilities. Furthermore, we furnish a high-quality dataset in English and 12 other languages, analyse the approach's feasibility in a multilingual context, provide extensive experimental results shedding light on good and bad practices for lyrics generation, and propose metrics to compare methods in the future.

cs.CL↗