Are You Sure You're Sure? On the Impact of Instruction Tuning on Confidence and Lexical Diversity
Instruction-tuned language models achieve strong performance across a range of generation tasks, but have also recently been shown to exhibit verbalized overconfidence. In question answering, verbalized model overconfidence may be associated with the consistency of the generated supporting rationales. In this paper, we study whether corresponding changes in the lexical diversity of generated answer rationales accompany changes in model confidence induced by instruction tuning. We evaluate three matched base and instruction-tuned models across question-answering benchmarks and find that instruc
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- PossiblePossibly related (embedding) · 46%Understanding large language models demands distinguishing human projection from machine cognition - Nature →
- LinkedLinked via arxiv author · 85%Irina Proskurina →
“Are You Sure You're Sure? On the Impact of Instruction Tuning on Confidence and Lexical Diversity”
- LinkedLinked via arxiv author · 85%Mayank Kumar →
“Are You Sure You're Sure? On the Impact of Instruction Tuning on Confidence and Lexical Diversity”
- LinkedLinked via arxiv author · 85%Oyindolapo O. Komolafe →
“Are You Sure You're Sure? On the Impact of Instruction Tuning on Confidence and Lexical Diversity”
- PossiblePossibly related (embedding) · 61%Causal evidence that language models use confidence to drive behaviour →
