@stanfordnlp: .@stanfordnlp grads at work!
Summary
Stanford NLP grads highlight research showing that language models, regardless of size or modality, suffer from overconfidence and overlooking details in uncommon scenarios.
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Cached at: 09/30/26, 03:58 AM
.@stanfordnlp grads at work!
Peng Qi (@qi2peng2): We found that LMs, large or small, text-only or multimodal, are susceptible to a shared problem – overconfidence and oblivion to details.
When presented with a scenario that is uncommon with overwhelming pretraining data that encourages a
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