GPT produces click distributions significantly different from real humans in 53% of UX first-click tasks, with prompting techniques like personas and chain-of-thought failing to improve alignment.
URL https://www.nature.com/articles/ s41586-024-07930-y
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Proposes RAP, a retrieval-based approximate prior method, to predict performance of symbolic programs and LLM prompts on new tasks using a Bernoulli model and corpus-derived performance distributions.
The paper introduces a reproducible optimization protocol for prompt-based LLM workflows in evidence synthesis that separates task definitions from prompt harnesses, optimizes the harness against metrics and examples, and preserves the result as an inspectable artefact.
citing papers explorer
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What Would GPT Click: Practical Effects of Human-AI Behavioral Misalignment and the Cost of Synthetic Participants in User Experience
GPT produces click distributions significantly different from real humans in 53% of UX first-click tasks, with prompting techniques like personas and chain-of-thought failing to improve alignment.
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Predicting Performance of Symbolic and Prompt Programs with Examples
Proposes RAP, a retrieval-based approximate prior method, to predict performance of symbolic programs and LLM prompts on new tasks using a Bernoulli model and corpus-derived performance distributions.
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A Reproducible Optimisation Protocol for Calibrating Prompt-Based Large Language Model Workflows in Evidence Synthesis
The paper introduces a reproducible optimization protocol for prompt-based LLM workflows in evidence synthesis that separates task definitions from prompt harnesses, optimizes the harness against metrics and examples, and preserves the result as an inspectable artefact.