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[2602.13568] Who Do LLMs Trust? Human Experts Matter More Than Other LLMs
Summary
This paper explores how large language models (LLMs) prioritize feedback from human experts over other LLMs in decision-making tasks, revealing significant implications for AI trust and credibility.
Why It Matters
Understanding how LLMs interpret and prioritize information from different sources is crucial for developing more reliable AI systems. This research highlights the importance of human expertise in shaping LLM responses, which can impact applications in various fields such as education, healthcare, and automated decision-making.
Key Takeaways
- LLMs show a preference for responses attributed to human experts over those from other LLMs.
- Expert framing significantly influences LLM decision-making, even when the expert's input is incorrect.
- The study indicates a form of credibility-sensitive social influence in LLMs across various decision domains.
Computer Science > Artificial Intelligence arXiv:2602.13568 (cs) [Submitted on 14 Feb 2026] Title:Who Do LLMs Trust? Human Experts Matter More Than Other LLMs Authors:Anooshka Bajaj, Zoran Tiganj View a PDF of the paper titled Who Do LLMs Trust? Human Experts Matter More Than Other LLMs, by Anooshka Bajaj and Zoran Tiganj View PDF HTML (experimental) Abstract:Large language models (LLMs) increasingly operate in environments where they encounter social information such as other agents' answers, tool outputs, or human recommendations. In humans, such inputs influence judgments in ways that depend on the source's credibility and the strength of consensus. This paper investigates whether LLMs exhibit analogous patterns of influence and whether they privilege feedback from humans over feedback from other LLMs. Across three binary decision-making tasks, reading comprehension, multi-step reasoning, and moral judgment, we present four instruction-tuned LLMs with prior responses attributed either to friends, to human experts, or to other LLMs. We manipulate whether the group is correct and vary the group size. In a second experiment, we introduce direct disagreement between a single human and a single LLM. Across tasks, models conform significantly more to responses labeled as coming from human experts, including when that signal is incorrect, and revise their answers toward experts more readily than toward other LLMs. These results reveal that expert framing acts as a strong prior...