Text-to-SQL conversion is a vital aspect of Natural Language Processing (NLP) that enables users to query databases using everyday language rather than technical SQL commands. This process is highly significant as it allows individuals to interact with complex databases seamlessly, regardless of their technical expertise. The challenge lies between natural language queries and the structured… →

Achieving high-fidelity waveform generation in audio synthesis is a significant challenge, particularly due to the slow inference times associated with traditional models like Conditional Flow Matching (CFM), which require numerous Ordinary Differential Equation (ODE) steps. While excellent in quality, these models are often too slow for real-time use. To solve this problem, a team of… →

High-fidelity waveform generation, particularly in text-to-speech (TTS) and audio generation applications, involves several critical challenges. Accurately generating natural-sounding audio remains a primary issue, essential for real-world deployment. Capturing the natural periodicity of high-resolution waveforms and producing high-quality output without artifacts such as metallic sounds or hissing noises is difficult. Additionally, slow inference speed limits the… →

Cloud AI infrastructure is vital to modern technology, providing the backbone for various AI workloads and services. Ensuring the reliability of these infrastructures is crucial, as any failure can lead to widespread disruption, particularly in large-scale distributed systems where AI workloads are synchronized across numerous nodes. This synchronization means that a failure in one node… →

Language models (LMs) have gained significant prominence in computational text analysis, offering enhanced accuracy and versatility. However, a critical challenge persists: ensuring the validity of measurements derived from these models. Researchers face the risk of misinterpreting results, potentially measuring unintended factors such as incumbency instead of ideology, or party names rather than populism. This discrepancy… →

As LLMs become increasingly complex and powerful, their inference process, i.e., generating text given a prompt, becomes computationally expensive and time-consuming. Many applications, such as real-time translation, dialogue systems, or interactive content generation, require quick responses. Additionally, slow inference consumes substantial computational resources, leading to higher operational costs.  Researchers from the Dalian University of Technology,… →

Large Multimodal Models (LMMs) are rapidly advancing, driven by the need to develop artificial intelligence systems capable of processing and generating content across multiple modalities, such as text and images. These models are particularly valuable in tasks that require a deep integration of visual and linguistic information, such as image captioning, visual question answering, and… →