Large language models (LLMs) can solve complex puzzles in seconds, yet they sometimes struggle over simple conversations. When these AI tools make assumptions, overlook key details, or neglect to ask clarifying questions, the result can erode trust and derail real-world interactions, where nuance is everything.
Why LLMs Struggle With Conversations
The primary reason LLMs struggle with conversation lies in their training methods. Most benchmarks use isolated, single-turn prompts with clear instructions. Training methods tend to optimize for the model’s next response, not its contribution to a successful, multi-turn exchange. Real-world interaction is dynamic and collaborative; it relies on context, clarification, and shared understanding.
A User-Centric Approach to Training
To address this, we’re exploring ways to train LLMs with users in mind. Our approach places models in simulated environments that reflect the back-and-forth nature of real conversations. Through reinforcement learning, these models improve through trial and error – learning when to ask questions and how to adapt tone and communication style to different situations. This user-centric approach helps bridge the gap between how LLMs are typically trained and how people actually use them.
Introducing CollabLLM
The post CollabLLM: Teaching LLMs to collaborate with users appeared first on Microsoft Research.
The concept behind CollabLLM, recipient of an ICML 2025 Outstanding Paper Award, is that in a constructive collaboration, the value of a response isn’t just in its immediate usefulness, but in how it contributes to the overall success of the conversation. A clarifying question might seem like a delay but often leads to better outcomes. A quick answer might appear useful but can create confusion or derail the interaction.
CollabLLM uses simulated multi-turn interactions and reinforcement learning during training. The model learns not just to respond, but to actively participate in a collaborative dialogue, asking clarifying questions when needed and adapting its communication style based on user feedback. This shift from passive responders to active collaborators represents a significant step toward building more trustworthy and effective AI systems.
Summary: Recipient of an ICML 2025 Outstanding Paper Award, CollabLLM improves how LLMs collaborate with users, including knowing when to ask questions and how to adapt tone and communication style to different situations. This approach helps move AI toward more user-centric and trustworthy systems.
The post CollabLLM: Teaching LLMs to collaborate with users appeared first on Microsoft Research.
Source: Read the original article here.
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