LLMs correct only 34.8% of zero-shot annotation errors via prompting, and Definition-Specific Familiarity correlates positively with performance (partial r = +0.41) while memorization metrics do not.
On second thought, let ' s not think step by step! bias and toxicity in zero-shot reasoning
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
cs.CL 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
LLMs outperform humans in expressing illocutionary intents and sycophancy in successful persuasive counter-arguments from ChangeMyView, with crowd workers preferring LLM versions.
Reasoning in large output spaces proceeds via shortlisting then fine-grained reasoning; this characterization enables a mechanistic distillation strategy that outperforms standard distillation.
citing papers explorer
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On the Limits of LLM Adaptability: Impact of Model-Internalized Priors on Annotation Task Performance
LLMs correct only 34.8% of zero-shot annotation errors via prompting, and Definition-Specific Familiarity correlates positively with performance (partial r = +0.41) while memorization metrics do not.
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"I understand your perspective": LLM Persuasion and Sycophancy through the Lens of Communicative Action Theory
LLMs outperform humans in expressing illocutionary intents and sycophancy in successful persuasive counter-arguments from ChangeMyView, with crowd workers preferring LLM versions.
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Characterize Then Distill: Mechanistic Reasoning in Large Output Spaces
Reasoning in large output spaces proceeds via shortlisting then fine-grained reasoning; this characterization enables a mechanistic distillation strategy that outperforms standard distillation.