PlatoLM: Teaching LLMs via a Socratic Questioning User Simulator
Chuyi Kong, Yaxin Fan, Xiang Wan, Feng Jiang, Benyou Wang
The unparalleled performance of closed-sourced ChatGPT has sparked efforts towards its democratization, with notable strides made by leveraging real user and ChatGPT conversations, as evidenced by Vicuna. However, due to challenges in gathering conversations involving human participation, current endeavors like Baize and UltraChat aim to automatically generate conversational data. They primarily rely on ChatGPT conducting roleplay to simulate human behaviors based on instructions rather than genuine learning from humans, resulting in limited scope, diminished diversity, and an absence of genuine multi-round conversational dynamics. To address the above issues, we target human questions extracted from genuine human-machine conversations as a learning goal and train a user simulator called `Socratic' to produce a high-quality human-centric synthetic conversation dataset. Subsequently, this dataset was used to train our assistant model, named `PlatoLM'. Experimentally, PlatoLM outpaces baseline models in both Vicuna-Bench and MT-Bench by pairwise comparison when considering equivalent training set sizes, and manual evaluation also shows that our model is highly competitive. Impressively, when fine-tuned with the latest LLaMA 2 model, PlatoLM achieves the SOTA performance among 7B models (including LLaMA-2-7B-chat and Vicuna-7B) in MT-Bench benchmark and in Alpaca-Eval benchmark, it ranks second among 7B models, even beating some larger scale models (including LLaMA-2-13B-chat and GPT-3.5). Further in-depth analysis demonstrates the scalability and transferability of our approach. The code is available at https://github.com/FreedomIntelligence/PlatoLM.