⭐ If you find this project helpful, please give us a star on GitHub! It means a lot to us. Github: https://github.com/Bairong-Xdynamics/Benchmarking-LLM-Realistic-Selling-Skill/ CustomerLM is a fine-tuned large language model based on Qwen, trained to play the customer side of a realistic sales conversation. It is the user simulator of the SalesLLM benchmark. Most evaluations of «can an LLM sell?» put a general-purpose model (e.g. GPT-4o) on the customer side. That is the weak link: general instruct models are trained to be helpful, so when they are told to role-play a buyer they drift into assisting the salesperson — volunteering objections’ answers, summarizing benefits, even pitching the product back. We call this role inversion, and it happens in 17.44% of GPT-4o-simulated dialogues. A user simulator that behaves like a salesperson makes every downstream sales score unreliable. CustomerLM is trained specifically to stay in character as a buyer: skeptical when the persona says skeptical, terse, self-interested, and willing to walk away. It cuts role inversion to 8.8% while producing responses measurably closer to real human customers. CustomerLM takes the customer persona as…
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Автор: MultiSense
Теги: qwen3, causal-lm, qwen, finetune, user-simulator, role-play, sales, dpo
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Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
