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arXiv Machine Learning By Jiamu Zhang, Tianze Yang, Yucheng Shi, Evan Chen, Zixiang Nie, Kelly Wan, Liangjie Hong, Ninghao Liu, Liang Wu

AnyJev Technical Report

Read the original on arXiv Machine Learning →

The report introduces AnyJev, a method that extracts typed decisions from a pretrained instruction‑tuned language model by restricting the next‑token distribution to a set of option tokens. It corrects two defects—label bias and position bias—without any gradient updates by dividing out a label prior and averaging log‑probabilities over cyclic rotations of the option list. Experiments on two 20‑option tasks show that rotations reduce order‑flip rates and improve accuracy across all tested models, while a stopping rule reduces the number of required rotations and increases decision throughput.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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