During conversations, the human flow of thoughts may result in topic shifts and evolution. In open-domain dialogue systems, it is crucial to track the topics discussed and recommend relevant topics to be included in responses to have effective conversations. Furthermore, topic evolution is needed to prevent stagnation as conversation length increases. Existing open-domain dialogue systems do not pay sufficient attention to topic evolution and shifting, resulting in performance degradation due to ineffective responses as conversation length increases. To address the shortcomings of existing approaches, we propose EvolvConv. EvolvConv conducts real-time conversation topic and user preference tracking and utilizes the tracking information to evolve and shift topics depending on conversation status. We conduct extensive experiments to validate the topic evolving and shifting capabilities of EvolvConv as conversation length increases. Un-referenced evaluation metric UniEval compare EvolvConv with the baselines. Experimental results show that EvolvConv maintains a smooth conversation flow without abruptly shifting topics; the probability of topic shifting ranges between 5%-8% throughout the conversation. EvolvConv recommends 4.77% more novel topics than the baselines, and the topic evolution follows balanced topic groupings. Furthermore, we conduct user surveys to test the practical viability of EvolvConv. User survey results reveal that responses generated by EvolvConv are preferred 47.8% of the time compared to the baselines and comes second to real human responses.

Chain-of-Thought (CoT) prompting enables large language models to solve complex reasoning problems by generating intermediate steps. However, confined by its inherent single-pass and sequential generation process, CoT heavily relies on the initial decisions, causing errors in early steps to accumulate and impact the final answers. In contrast, humans adopt recursive thinking when tackling complex reasoning problems, i.e. iteratively breaking the original problem into approachable sub-problems and aggregating their answers to resolve the original one. Inspired by the human cognitive process, we propose SOCRATIC QUESTIONING, a divide-and-conquer style algorithm that mimics the recursive thinking process. Specifically, SOCRATIC QUESTIONING leverages large language models to raise and answer sub-questions until collecting enough information to tackle the original question. Unlike CoT, SOCRATIC QUESTIONING explicitly navigates the thinking space, stimulates effective recursive thinking, and is more robust towards errors in the thinking process. Extensive experiments on several complex reasoning tasks, including MMLU, MATH, LogiQA, and visual question-answering demonstrate significant performance improvements over the state-of-the-art prompting methods, such as CoT, and Tree-of-Thought. The qualitative analysis clearly shows that the intermediate reasoning steps elicited by SOCRATIC QUESTIONING are similar to humans’ recursively thinking process of complex reasoning problems.