A 14-code content model for local post-hoc AI explanations, derived from 325 user statements and validated by experts with high reliability scores.
Reliability in Content Analysis: Some Com- mon Misconceptions and Recommendations
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ProactBench measures LLM conversational proactivity in three phases using 198 multi-agent dialogues and finds recovery behavior hard to predict from existing benchmarks.
A theory-grounded taxonomy of eight communication roles enables scalable annotation via LLMs and outperforms baselines when predicting peer recognition in student teams and performance improvement on a public deliberation dataset.
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What Should Explanations Contain? A Human-Centered Explanation Content Model for Local, Post-Hoc Explanations
A 14-code content model for local post-hoc AI explanations, derived from 325 user statements and validated by experts with high reliability scores.
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ProactBench: Beyond What The User Asked For
ProactBench measures LLM conversational proactivity in three phases using 198 multi-agent dialogues and finds recovery behavior hard to predict from existing benchmarks.
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Who Plays Which Role When? Communication Role Dynamics for Peer Recognition and Team Performance Prediction
A theory-grounded taxonomy of eight communication roles enables scalable annotation via LLMs and outperforms baselines when predicting peer recognition in student teams and performance improvement on a public deliberation dataset.