Lost in Conversation is a code repository designed for benchmarking large language models (LLMs) on multi-turn task completion, enabling the reproduction of experiments from the paper "LLMs Get Lost in Multi-Turn Conversation." It includes tools for simulating conversations across various tasks, a web-based viewer, and instructions for integrating with LLMs. The repository is intended for research purposes and emphasizes careful evaluation and oversight of outputs to ensure accuracy and safety.

Researchers at Google have developed a benchmarking pipeline and synthetic personas to evaluate the performance of large language models (LLMs) in diagnosing tropical and infectious diseases (TRINDs). Their findings highlight the potential for LLMs to enhance clinical decision support, especially in low-resource settings, while also identifying the need for ongoing evaluation to ensure accuracy and cultural relevance.