The field of natural language processing is moving towards more robust and reliable evaluation methods for large language models (LLMs). Current research focuses on developing efficient and effective approaches to assess the quality of LLMs, including dialogue evaluation, sentiment analysis, and question difficulty estimation. Noteworthy papers in this area include Learning an Efficient Multi-Turn Dialogue Evaluator from Multiple Judges, which proposes an efficient method for aggregating multiple LLM judges to evaluate dialogue quality. GrandJury introduces a collaborative machine learning model evaluation protocol for dynamic quality rubrics, enabling pluralistic and accountable evaluation of LLM outputs.