One of the most pressing challenges in the evaluation of Vision-Language Models (VLMs) is related to not having comprehensive benchmarks that assess the full spectrum of model capabilities. This is because most existing evaluations are narrow in terms of focusing on only one aspect of the respective tasks, such as either visual perception or question The post Holistic Evaluation of Vision Language Models (VHELM): Extending the HELM Framework to VLMs appeared first on MarkTechPost.

The current challenges in text-to-speech (TTS) systems revolve around the inherent limitations of autoregressive models and their complexity in aligning text and speech accurately. Many conventional TTS models require complex elements such as duration modeling, phoneme alignment, and dedicated text encoders, which add significant overhead and complexity to the synthesis process. Furthermore, previous models like The post F5-TTS: A Fully Non-Autoregressive Text-to-Speech System based on Flow Matching with Diffusion Transformer (DiT) appeared first on MarkTechPost.

Recent progress in LLMs has spurred interest in their mathematical reasoning skills, especially with the GSM8K benchmark, which assesses grade-school-level math abilities. While LLMs have shown improved performance on GSM8K, doubts remain about whether their reasoning abilities have truly advanced, as current metrics may only partially capture their capabilities. Research suggests that LLMs rely on The post Apple Researchers Introduce GSM-Symbolic: A Novel Machine Learning Benchmark with Multiple Variants Designed to Provide Deeper Insights into the Mathematical Reasoning Abilities of LLMs appeared first on MarkTechPost.