3 papers
cs.LG2026
FORESTLLM: Large Language Models Make Random Forest Great on Few-shot Tabular Learning
Zhihan Yang, Jiaqi Wei, Xiang Zhang +6
Tabular data high-stakes critical decision-making in domains such as finance, healthcare, and scientific discovery. Yet, learning effectively from tabular data in few-shot settings…
cs.SD2025
GLM-TTS Technical Report
Jiayan Cui, Zhihan Yang, Naihan Li +10
This work proposes GLM-TTS, a production-level TTS system designed for efficiency, controllability, and high-fidelity speech generation. GLM-TTS follows a two-stage architecture, c…
cs.LG2025
Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Models
Marianne Arriola, Aaron Gokaslan, Justin T. Chiu +5
Diffusion language models offer unique benefits over autoregressive models due to their potential for parallelized generation and controllability, yet they lag in likelihood modeli…