The article explains how to assess fine‑tuned large models using both human judgment and dataset‑driven metrics, outlines common pitfalls, introduces benchmark datasets and evaluation frameworks, and provides concise answers to related interview questions.
This article introduces EvalScope, an open‑source framework for evaluating large language models, detailing its architecture, built‑in benchmarks, installation steps, and step‑by‑step guides for both performance stress testing and dataset‑based capability assessment, enabling users to independently verify model quality without relying on official documentation.
Google’s new ScreenAI visual‑language model, built on the PaLI architecture, can interpret user interfaces and infographics, answer UI‑related questions, generate summaries and navigate screens, and sets new benchmarks that may reshape future user‑experience research and applications.
GraphEdit leverages large language models and a lightweight edge predictor to remove noisy connections and uncover hidden node dependencies, achieving state‑of‑the‑art performance on benchmark graph datasets such as Cora, Citeseer, and PubMed, while demonstrating strong robustness to noise and limited supervision.
This article surveys recent advances in graph neural network explainability, systematically categorizing instance‑level and model‑level methods, reviewing datasets, evaluation metrics, and proposing new benchmark graph datasets for interpretable GNN research, and highlighting future research directions.
