Facilitating Human-LLM Collaboration through Factuality Scores and Source Attributions

Understand

While humans increasingly rely on large language models (LLMs), they are susceptible to generating inaccurate or false information, also known as "hallucinations".

• Technical advancements have been made in algorithms that detect hallucinated content by assessing the factuality of the model's responses and attributing sections of those responses to specific source documents.
• However, there is limited research on how to effectively communicate this information to users in ways that will help them appropriately calibrate their trust toward LLMs.
• To address this issue, we conducted a scenario-based study (N=104) to systematically compare the impact of various design strategies for communicating factuality and source attribution on participants' ratings of trust, preferences, and ease in validating response accuracy.