Multi Turn

Multi-turn interactions with large language models (LLMs) are a burgeoning research area focusing on improving LLMs' ability to engage in extended, contextually aware conversations. Current research emphasizes developing robust methods for evaluating and enhancing LLMs' performance in these multi-turn scenarios, including techniques like reinforcement learning, contrastive learning, and fine-tuning strategies tailored to multi-turn dialogue. This research is crucial for advancing the safety and reliability of LLMs, particularly in addressing vulnerabilities to adversarial attacks and improving their ability to adapt to user needs and provide helpful, coherent responses across multiple conversational turns. The resulting improvements will have significant implications for various applications, including education, customer service, and more generally, human-computer interaction.