mlsanityData to 7 Oct 2026

Seeing the Reasoning: How LLM Rationales Influence User Trust and Decision-Making in Factual Verification Tasks

arXiv 2603.07306 ↗First posted 7 Mar 2026cs.HCcited by 1

On the map: LLM reasoning → Reasoning benchmarks and evaluation

Abstract (arXiv)

Large Language Models (LLMs) increasingly show reasoning rationales alongside their answers, turning "reasoning" into a user-interface element. While step-by-step rationales are typically associated with model performance, how they influence users' trust and decision-making in factual verification tasks remains unclear. We ran an online study (N=68) manipulating three properties of LLM reasoning rationales: presentation format (instant vs. delayed vs. on-demand), correctness (correct vs. incorrect), and certainty framing (none vs. certain vs. uncertain). We found that correct rationales and certainty cues increased trust, decision confidence, and AI advice adoption, whereas uncertainty cues reduced them. Presentation format did not have a significant effect, suggesting users were less sensitive to how reasoning was revealed than to its reliability. Participants indicated they use rationales to primarily audit outputs and calibrate trust, where they expected rationales in stepwise, adaptive forms with certainty indicators. Our work shows that user-facing rationales, if poorly designed, can both support decision-making yet miscalibrate trust.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 10 of its 33 references that are in the map. First posted on arXiv 7 Mar 2026.

Builds on

10 of 33 references.

Closest ancestors · 1

Same direction, newest first

  1. 2025Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models16 Jan

Other references · 9

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  2. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
  3. 2021Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategiescited by 736
  4. 2019"How do I fool you?": Manipulating User Trust via Misleading Black Box Explanationscited by 111
  5. 2022Explanations Can Reduce Overreliance on AI Systems During Decision-Makingcited by 111
  6. 2019How model accuracy and explanation fidelity influence user trustcited by 44
  7. 2023Two Failures of Self-Consistency in the Multi-Step Reasoning of LLMscited by 36
  8. 2026Reasoning Models Will Sometimes Lie About Their Reasoningcited by 4
  9. 2023Do You Trust What You See? Toward A Multidimensional Measure of Trust in Visualizationcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Design Theater: Evaluating the Gap Between User-Facing Design Reasoning and Implementation in Generative UI Toolscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026When LLM Rationales Become User-Facing: Effects on Trust Perception, Decision-Making, and Gaze Behaviors
  2. 2026Evaluating the False Trust Engendered by LLM Explanations
  3. 2026Do Reasoning Representations Help Humans Evaluate LLM Outputs?
  4. 2026Graphionale: How Graph Visualizations of LLM Rationales Affect Human Decision Making
  5. 2026Not All Uncertainty Is Equal: How Uncertainty Granularity Shapes Human Verification in LLM-Assisted Decision Making ↗
  6. 2025Critical or Compliant? The Double-Edged Sword of Reasoning in Chain-of-Thought Explanations
  7. 2026Distortion Instead of Hallucination: The Effect of Reasoning Under Strict Constraints
  8. 2024What Large Language Models Know and What People Think They Know