5 accepted papers
Geometry problem solving presents distinctive challenges in artificial intelligence, requiring exceptional multimodal comprehension and rigorous mathematical reasoning capabilities. Existing approaches typically fall into two categories: neural-based and symbolic-based methods, both of which exhibit…
Consistent image generation requires faithfully preserving identities, styles, and logical coherence across multiple images,which is essential for applications such as storytelling and character design.Supervised training approaches struggle with this task due to the lack of large-scale datasets cap
Recent advancements in large language models (LLMs) have markedly improved their capacity to handle long text inputs; however, current models, including GPT-4o, still exhibit unsatisfactory performance in long-form generation. Generating high-quality long-form content still remains a significant cha…
Fine-tuning is a crucial process for adapting large language models (LLMs) to diverse applications. In certain scenarios, such as multi-tenant serving, deploying multiple LLMs becomes necessary to meet complex demands. Recent studies suggest decomposing a fine-tuned LLM into a base model and corresp…
LoRA employs lightweight modules to customize large language models (LLMs) for each downstream task or domain, where different learned additional modules represent diverse skills. Combining existing LoRAs to address new tasks can enhance the reusability of learned LoRAs, particularly beneficial for…