Hugging Face Trending Papers
AnyJev Technical Report
Read the original on Hugging Face Trending Papers →The Flow has not summarised this story yet — read it at Hugging Face Trending Papers.
Related stories
arXiv Machine Learning
Oct 3
AnyJev Technical Report
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.
Hugging Face Trending Papers
Sep 22
Type-Safe Is Not Error-Free: A Constrained Decision Head Follows the Option Name, Not the Rubric Bound to It
Typed decision models are built for settings where model outputs are consumed directly by software. Instead of generating free-form text, they return a decision over a predefined set of options. By co...
arXiv AI
Sep 25
Type-Safe Is Not Error-Free: A Constrained Decision Head Follows the Option Name, Not the Rubric Bound to It
The study examines how renaming option labels in typed decision models affects model behavior. By swapping the names of two options (e.g., from 0/1 to no/yes) while keeping the underlying rubrics unchanged, the authors observed a dramatic shift in decision rankings—AUC dropped from .94 to .23 and answer flips increased by 70.4 per hundred. The effect is amplified with more options and depends on the semantic polarity of the labels, yet the models still maintain a zero type‑error rate.
arXiv AI
Oct 5
Labels Override Definitions in Jev-Style Typed Decision Models
arXiv:2610.02586v1 Announce Type: new Abstract: A typed decision model answers a fixed question about an input by returning a probability for each of several caller-defined options. Each option carri...
arXiv Computation and Language
6d ago
Readout Stability in Prefill-Only Decision Models:Zero-Label Prediction and Inference-Time Compute Allocation
Prefill‑only decision models evaluate every candidate in a menu in a single forward pass, avoiding decoding and reducing cost by one to two orders of magnitude compared to generative language models. The paper demonstrates that when only the candidate menu changes, the model’s post‑intervention accuracy can be predicted solely from the cached first‑pass distribution using a simple estimator that renormalizes and selects the argmax, without any labels or second pass. Across seven model families, ten datasets, and two task types, this menu‑only intervention prediction is within 4.2 points of actual accuracy, and in one family it is exact, whereas a probability‑level variant fails by 21 points, indicating the property resides in ranking rather than calibrated probabilities.
arXiv AI
Sep 25
NumericJev: Jev-like LLM Numerical Decoding with Multiway Decision Trees
NumericJev introduces a training‑free numerical decoding algorithm that allows large language models with Jev‑like structured‑choice interfaces to output precise numerical values. The method refines a numerical range using a multiway decision tree, achieving lower mean absolute error than direct selection from a candidate list. Experiments on an arithmetic benchmark show a 2.93 percentage‑point improvement, and a historical‑index study reports a 4.58% mean relative recall error with zero readout error when the value is supplied.