Beneficial Revision
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Easier to Mislead Than to Correct: Harmful and Beneficial Revision in LLM Conformity
arXiv:2606.01637v1 Announce Type: new Abstract: Large language models are increasingly used in multi-agent systems, where they see and respond to other agents' answers. A key risk is conformity: a model may abandon its own answer simply because others agree on a different one. Prior studies show that LLMs often revise toward a majority answer, but it remains unclear whether these revisions help correct mistakes as often as they introduce new errors.
Easier to Mislead Than to Correct: Harmful and Beneficial Revision in LLM Conformity
arXiv:2606.01637v2 Announce Type: replace Abstract: Large language models are increasingly used in multi-agent systems, where they see and respond to other agents' answers. A key risk is conformity: a model may abandon its own answer simply because others agree on a different one. Prior studies show that LLMs often revise toward a majority answer, but it remains unclear whether these revisions help correct mistakes as often as they introduce new errors.
AI Assistance for Discretionary Work: Increasing Feedback Provision in Higher Education
Announce Type: new Abstract: AI systems increasingly shape human workflows by generating intermediate artifacts that users can adopt, revise, or ignore. While prior work has shown that AI assistance can improve the efficiency and accuracy of required tasks, less is known about whether it can increase participation in discretionary but beneficial work that users often intend to perform but frequently skip. We study this question in the context of personalized feedback provision in higher...
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Generation AI: Schools in Asia are embracing artificial intelligence
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