arXiv: cs.AI preprintsInternational6 November 2026
Signals, Evidence, and Context: User Scrutiny in Real-World LLM Conversations
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arXiv:2611.08232v1 Announce Type: new Abstract: Large language models (LLMs) often present synthesized answers without making their supporting evidence easy to inspect. We study how features of these answers relate to users' subsequent accepting, checking, and challenging in 489,283 assistant-user response pairs from ShareChat and WildChat. Broad attribution language appears in 31.9% of qualifying responses, compared with explicit URLs in 1.33%, revealing a gap between claims of grounding and preserved inspectable support. Attribution, uncertainty, and other response cues generally accompany b
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