6 accepted papers
Influenced by context such as tone, emotion and demographic, pre-trained language models may generate harmful text, which limits their widespread application. While detoxifying language models seeks to reduce the likelihood of generating harmful content. There are two categories of detoxification st…
The widespread adoption of large language models (LLMs) across industries has increased the demand for high-quality and customizable outputs. However, traditional alignment methods often require retraining large pretrained models, making it difficult to quickly adapt and optimize LLMs for diverse ap…
With the rapidly expanding landscape of large language models, aligning model generations with human values and preferences is becoming increasingly important. Popular alignment methods, such as Reinforcement Learning from Human Feedback, have shown significant success in guiding models with greater…
Recently, tremendous strides have been made to align the generation of Large Language Models (LLMs) with human values to mitigate toxic or unhelpful content. Leveraging Reinforcement Learning from Human Feedback (RLHF) proves effective and is widely adopted by researchers. However, implementing RLHF…
Chinese spelling check (CSC) is a fundamental NLP task that detects and corrects spelling errors in Chinese texts. As most of these spelling errors are caused by phonetic similarity, effectively modeling the pronunciation of Chinese characters is a key factor for CSC. In this paper, we consider intr…
Noisy color image and guided near-infrared (NIR) image can be jointly employed to eliminate noise and enhance details. Existing methods mostly rely on explicit designed filters and hand-crafted objective function optimization. These methods usually introduce erroneous structures from guidance signal…