binary-classification

This article demonstrates how to use any GGUF model with llama.cpp for binary classification tasks, such as spam detection, by configuring parameters to output probabilities from logprobs.

ProbPlug introduces a lightweight framework for estimating confidence in LLM-based binary classification using internal token features, improving reliability across text and multimodal tasks without modifying the base model.

This paper demonstrates that monotone adversarial corruptions can make certain multiclass and partial binary classification problems unlearnable, providing tight bounds on corruption budgets and extending previous results on binary classification.

This study tests uncertainty sampling in active learning under bounded label noise, comparing error exposure and location effects across datasets to assess robustness and performance.

Raindrop launches Signals 2.0 powered by rd-signal-2, a new model pipeline for building task-specific binary classifiers from production traces. It claims near GPT-5.6 Sol xhigh accuracy at 1600x lower cost, and also introduces Signal Builder for custom classifiers with zero data retention.

This paper characterizes the inherent interpretability of linear models vs. single-qubit mixed-state models for binary classification, showing that the quantum model learns a hyperellipsoid instead of a hyperplane, with implications for inductive biases and pedagogy.

This paper proposes PUe, a framework for biased positive-unlabeled learning that uses normalized propensity scores and normalized inverse probability weighting to handle selection bias, improving classification under non-uniform label distributions.

This paper presents a spectral phase diagram for binary few-shot classification, analyzing intrinsic dimensionality and geometric saturation for representational diagnosis.

This paper presents machine learning frameworks for binary classification of road surface conditions (grip vs. slip) using production vehicle signals during cruising, addressing the limitations of traditional friction estimation methods that fail under low-slip conditions.

This paper explores which agreement statistics for LLM judge validation are redundant when criteria are binary, and provides a checklist for proper reporting including abstention handling.

This paper proposes using pairwise queries to improve selective classification for binary classification, particularly where confidence estimates are inconsistent, as in LLM in-context learning. Theoretical conditions and experiments on synthetic and real datasets show that pairwise query-based algorithms achieve better accuracy-cost tradeoffs than raw confidence estimates.

This paper presents findings from the Counter Turing Test shared task on AI-generated text detection, with top systems achieving perfect binary classification but significantly lower performance in model attribution, highlighting the difficulty of distinguishing outputs from different large language models.

This paper systematically evaluates five imbalance handling methods (RUS, ROS, SMOTE, re-weighting, direct F1 optimization) on three biomedical datasets (tabular, text, image) using models of varying complexity. Results show that benefits depend on model complexity and data modality, with ROS, re-weighting, and direct F1 optimization being effective for complex models on unstructured data.