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Found 2 papers, 2 papers with code
SH2: Self-Highlighted Hesitation Helps You Decode More Truthfully
Therefore, we propose to ''highlight'' the factual information by selecting the tokens with the lowest probabilities and concatenating them to the original context, thus forcing the model to repeatedly read and hesitate on these tokens before generation.
Syntax-guided Localized Self-attention by Constituency Syntactic Distance
Recent works have revealed that Transformers are implicitly learning the syntactic information in its lower layers from data, albeit is highly dependent on the quality and scale of the training data.
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