Paraphrase generation requires diverse generation of high-quality utterance by the given semantics, which is a challenge for traditional end-2-end text generation.

Inspired by the diffusion modeling for diverse image generations, a research team from Nanjing University led by Wei Zou managed to reconcile the quality and diversity for paraphrase generation via latent diffusion modeling (Latent Diffusion Paraphraser, LDP), and published their new research on 15 January 2026 in Frontiers of Computer Science co-published by Higher Education Press and Springer Nature.

Chain-of-Thought (CoT) prompting has enhanced the performance of Large Language Models (LLMs) across various reasoning tasks. However, CoT still falls short in dealing with complex math word problems, as it usually suffers from three pitfalls: semantic misunderstanding errors, calculation errors, and step-missing errors. Prior studies involve addressing the calculation errors and step-missing errors, but neglect the semantic misunderstanding errors, which is the major factor limiting the reasoning performance of LLMs.

With the rapid advancement of Large Language Models (LLMs), an increasing number of researchers are focusing on Generative Recommender Systems (GRSs). Unlike traditional recommendation systems that rely on fixed candidate sets, GRSs leverage generative capabilities, making them more effective in exploring user interests.