Denoising Language Model

Denoising language models (DLMs) aim to improve the quality and robustness of language processing by mitigating noise or errors in text or speech data. Current research focuses on leveraging large language models (LLMs) to enhance denoising capabilities, particularly in applications like recommendation systems, speech recognition, and detecting machine-generated text. This involves developing novel algorithms and architectures, such as incorporating noise information as a conditioner within LLMs or using diffusion models to refine the denoising process. The resulting improvements in accuracy and efficiency have significant implications for various fields, including information retrieval, human-computer interaction, and combating misinformation.