LLMs generate empathic responses using a predictable template of 10 tactics that matches 83-90% of outputs and covers most of each response, while human responses are more diverse.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
Babbling Suppression stops LLM code generation upon test passage to reduce token output and energy consumption by up to 65% across Python and Java benchmarks.
citing papers explorer
-
AI generates well-liked but templatic empathic responses
LLMs generate empathic responses using a predictable template of 10 tactics that matches 83-90% of outputs and covers most of each response, while human responses are more diverse.
-
Babbling Suppression: Making LLMs Greener One Token at a Time
Babbling Suppression stops LLM code generation upon test passage to reduce token output and energy consumption by up to 65% across Python and Java benchmarks.