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Princeton University
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Factual hallucinations in LLM chess commentary are alarmingly high, with smaller models making over 40% incorrect claims, even after tool augmentation.
ContextRL reveals that fine-grained context selection can lead to substantial performance boosts in LLMs, outperforming traditional data augmentation methods.
