RMCT matches the rate of target behaviors like bias-following across input perturbations to reduce sycophancy in LLMs while preserving verbalization of bias cues.
and Wong, Catherine and Feng, Jiahai and Wei, Megan and Tenenbaum, Joshua B
3 Pith papers cite this work, alongside 14 external citations. Polarity classification is still indexing.
3
Pith papers citing it
14
external citations · external index
representative citing papers
RoboWits benchmark with 238 tasks shows pre-trained VLAs succeed on seed tasks but fail on mutated ones, highlighting brittleness in reasoning.
Wrapping LLM calls as oracles in classical algorithms improves sorting and clustering accuracy for small models, but several advertised applications are missing from the experiments.
citing papers explorer
-
Consistency Training while Mitigating Obfuscation via Rate Matching
RMCT matches the rate of target behaviors like bias-following across input perturbations to reduce sycophancy in LLMs while preserving verbalization of bias cues.
-
RoboWits: Unexpected Challenges for Robotic Creative Problem Solving
RoboWits benchmark with 238 tasks shows pre-trained VLAs succeed on seed tasks but fail on mutated ones, highlighting brittleness in reasoning.
-
Verbalized Algorithms: Classical Algorithms are All You Need (Mostly)
Wrapping LLM calls as oracles in classical algorithms improves sorting and clustering accuracy for small models, but several advertised applications are missing from the experiments.