The field of large language models (LLMs) is moving towards more efficient and transparent solutions, with a focus on improving user experience and information retrieval on the web. Recent developments have led to the creation of open-source toolkits that facilitate the deployment of LLM-powered chatbots, allowing for more flexible and customizable integration into websites. Additionally, research has been conducted to investigate the self-preference bias in LLM evaluators, providing a more nuanced understanding of their strengths and limitations. The use of verifiable benchmarks has enabled the distinction between legitimate and harmful self-preference, highlighting the importance of accurate evaluation in LLM-based applications. Furthermore, the exploration of Constitutional AI has shown promise in reducing the need for human labeling and improving model harmlessness, although challenges remain in balancing helpfulness and harmlessness. Noteworthy papers include: Do LLM Evaluators Prefer Themselves for a Reason?, which provides key insights into the self-preference bias in LLM evaluators. Constitution or Collapse? Exploring Constitutional AI with Llama 3-8B, which investigates the effectiveness of Constitutional AI in improving model harmlessness.