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University of Michigan
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Counterfactual fairness in reinforcement learning can be achieved with a new preprocessing algorithm that ensures equitable access in high-stakes decision-making.
Token-level detection reveals the specific contributions of LLMs in collaborative writing, outperforming traditional document-level methods.
Single-rollout RL can rival multi-rollout performance for LLM reasoning, thanks to a new batchwise advantage estimation technique that dramatically improves value function accuracy.
Reasoning beats scale: a 1.5B parameter model, READER, outperforms models 100-1000x larger in detecting AI-generated text by explicitly generating a rationale for its decision.
Pinpointing exactly where humans end and LLMs begin in co-authored text is now possible, thanks to a clever adaptation of time-series change point detection.
