Seeking Information with RAG-Assistants: Does Model Size Matter in Human-AI Collaborations?

• URL: http://arxiv.org/abs/2605.00964v1
• Date: Fri, 01 May 2026 15:18:32 GMT
• Title: Seeking Information with RAG-Assistants: Does Model Size Matter in Human-AI Collaborations?
• Abstract summary: This work evaluates an assistant based on Retrieval-Augmented Generation (RAG) in a realistic multi-turn information-seeking scenario.<n>In this setting, we investigate how underlying model size shapes the human-AI collaborative dynamic.<n>Results show that the performance gain of human-AI collaboration over the model-only baselines is significant, irrespective of model size.
• Score: 2.824083651578116
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Much research on LLMs has focused on increasing benchmark performance. However, the evaluation of such models in real-world collaborative human-AI workflows has stayed behind. This work evaluates a chatbot-style assistant based on Retrieval-Augmented Generation (RAG) in a realistic multi-turn information-seeking scenario inspired by workplace settings where compliance with local legislation and secure handling of sensitive data are often key. Specifically, we examine the performance of humans (N=112) assisted by RAG-assistants compared to LLM-only or LLM+RAG baselines. In this setting, we investigate how underlying model size (3B, 8B, and 70B) shapes the human-AI collaborative dynamic and how it influences perceived usability and satisfaction. Results show that the performance gain of human-AI collaboration over the model-only baselines is significant, irrespective of model size, suggesting that hybrid systems are beneficial in information-seeking scenarios. Interestingly, however, perceived usability and satisfaction among participants showed little difference across model sizes. This demonstrates a nuanced trade-off between model size, performance, and user perception. Our work highlights the added value of evaluating AI applications in actual multi-turn interactions with human users, looking at usability and satisfaction besides accuracy, rather than focusing on benchmark performance only.

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