CollabLLM: From Passive Responders to Active Collaborators

Lay Summary:

Many people use AI chatbot (language models) to write, code, or solve problems, but current language models often fall short in real conversations. They tend to respond passively to vague questions instead of helping users clarify their goals and drag users into frustrating and inefficient interactions because they don’t plan ahead.We introduce CollabLLM, a new training method that teaches the language model to look several turns into the future. During training we simulate whole conversations and give each reply a “multiturn-aware reward” based on how much it helps the conversation in the future. This reward encourages the language model to ask clarifying questions, surface missing details, and offer constructive next steps.CollabLM was tested on writing assistance, coding, and math tutoring. It beat strong baselines on task success and on how interative and efficient the conversations are. In a study with 201 real users, it raised satisfaction scores and reduced the time needed to finish tasks by 10%. CollabLLM makes everyday language model assistants more proactive, efficient, and genuinely user-centered.