Since the advent of personal computing devices, intelligent personal assistants (IPAs) have
been one of the key technologies that researchers and engineers have focused on, aiming …

The remarkable progress of Multi-modal Large Language Models (MLLMs) has garnered
unparalleled attention, due to their superior performance in visual contexts. However, their …

Multi-modal Large Language Models (MLLMs) have recently emerged as a significant focus
in academia and industry. Despite their proficiency in general multi-modal scenarios, the …

Recently, video object segmentation (VOS) referred by multi-modal signals, eg, language
and audio, has evoked increasing attention in both industry and academia. It is challenging …

Conventional recommendation methods have achieved notable advancements by
harnessing collaborative or sequential information from user behavior. Recently, large …

We propose a novel persona-driven data synthesis methodology that leverages various
perspectives within a large language model (LLM) to create diverse synthetic data. To fully …

One way to enhance the reasoning capability of Large Language Models (LLMs) is to
conduct Supervised Fine-Tuning (SFT) using Chain-of-Thought (CoT) annotations. This …

Mathematical reasoning presents a significant challenge for Large Language Models
(LLMs) due to the extensive and precise chain of reasoning required for accuracy. Ensuring …

Large language models (LLMs) have exhibited great potential in mathematical reasoning.
However, there remains a performance gap in this area between existing open-source …

Neural Code Intelligence--leveraging deep learning to understand, generate, and optimize
code--holds immense potential for transformative impacts on the whole society. Bridging the …