Recent research in AI has focused on improving the evaluation of large language models (LLMs). A new paper led by @pat_verga suggests using an ensemble of smaller LLMs, termed a Panel of LLM Evaluators (PoLL), which is less biased, faster, and seven times cheaper than using a single large model. This approach has shown effectiveness in QA and Arena-hard evaluations. Additionally, @scale_AI introduced a new evaluation benchmark, GSM1K, to address overfitting in popular LLMs. Another significant development is the open-source Prometheus 2, which includes models like 7B & 8x7B, designed to mirror human and GPT-4 judgments closely. These models support both direct assessments and pairwise ranking, as well as user-defined assessments, enhancing transparency, controllability, and affordability in LLM evaluations.