Super-resolution (SR) is a long-standing task in the field of computer vision that aims to improve the quality and resolution of an image. ESRGAN is a representative generative adversarial network specialized to produce perceptually convincing SR ima...
Direct preference optimization (DPO) has been successfully applied to align large language models (LLMs) with human preferences. In recent years, DPO has also been used to improve the generation quality of text-to-image diffusion models. However, exi...
High-quality synthetic text can mitigate annotation scarcity in text classification. However, standard preference optimization often produces samples that are fluent but weakly label-specific. We present Utility-weighted Direct Preference Optimizatio...
While reinforcement learning is widely used to align large language models (LLMs), its application to open-ended settings remains limited by reliance on preference datasets, making it difficult to scale to non-reasoning tasks such as safety. Applying...
Affective Image Stylization (AIS) converts an emotional intent into executable artistic visual styles. Existing methods are often limited to discrete label settings and provide limited interpretability of how target emotions are realized. We propose...
Diffusion Direct Preference Optimization (DPO) provides a reward-free route for aligning text-to-image diffusion models with human preferences, but its supervision is commonly restricted to fixed winner–loser pairs. When dispreferred examples c...
Reinforcement Learning from Human Feedback (RLHF) produces powerful instruction-following models but relies on a preference-labeling process that is both costly and slow. An effective alternative, Reinforcement Learning from AI Feedback (RLAIF), uses...
Low-resource languages remain underserved by contemporary large language models (LLMs) because they lack sizable corpora, bespoke preprocessing tools, and the computing budgets assumed by mainstream alignment pipelines. Focusing on Kazakh, we present...
Ensuring that Large Language Models align with human values of honesty is a critical challenge, particularly due to the scarcity of labeled data for distinguishing known versus unknown knowledge boundaries. We propose a weak-to-strong generalization...
Large Language Models (LLMs) have immensely advanced the field of Artificial Intelligence (AI), with recent models being able to perform chain-of-thought reasoning and solve complex mathematical problems, ranging from theorem proving to ones involvin...
Offline reinforcement learning (offline RL) and large-language-model (LLM) alignment are typically studied as independent domains and each has developed its own pairwise comparison technique. Prior studies have validated pairwise classification exclu...
We propose QwenMoE-SC, a semantic communication framework that integrates a Mixture-of-Experts (MoE) Large Language Model with three complementary modules: (1) a Graph Neural Network (GNN)-based Unequal Error Protection (UEP) plug-in that assigns sem...
As the application of high-altitude solar drones expands, ensuring their safety is paramount. Traditional safety modeling, which relies on manual expert analysis, struggles to keep pace with rapid development cycles. While Large Language Models (LLMs...
Imaging logging serves as a critical technology for identifying and characterizing fractures in unconventional oil and gas reservoirs. Despite significant progress in deep learning for automated fracture recognition in this field, the integration of...
Large Language Models (LLMs) have achieved substantial progress in knowledge-intensive tasks, particularly through Retrieval-Augmented Generation (RAG) frameworks. However, existing RAG systems often suffer from performance degradation when input que...
Today, the impact of information technologies (IT) on humanity in this artificial intelligence (AI) era is vast, transformative, and remarkable, especially on human beliefs, practices, and truth discovery. Modern IT advancements like AI, particularly...
The efficient management and utilization of coal mine equipment maintenance knowledge is an indispensable foundation for advancing the establishment of intelligent mines. This knowledge has problems such as scattered, low sharing, and insufficient ma...