classification

This paper discusses methods for selecting features to improve the classification of multiword expressions.

This paper introduces PLACO, a framework for selecting cost-effective subsets of humans to collaborate with AI models in classification tasks, balancing performance and human labeling costs.

This content covers methodologies for categorizing amino acids, likely involving computational or biological analysis techniques.

Authors propose a 2D early-exit method that jointly trims layers and input sentences, yielding 1.4–2.3× extra speed-up on sentiment tasks across Llama 3.1/3.2, Gemma and Qwen models.

Researchers from National Taiwan University propose replacing fixed translation-based prompting strategies in multilingual LLMs with lightweight learned classifiers that route each instance to either native or translation-based prompting. Their analysis across 10 languages and 4 benchmarks shows no single strategy is universally optimal, with translation benefiting low-resource languages most, and the learned routing achieving statistically significant improvements over fixed strategies.

Interfaze AI introduces a specialized model that surpasses general LLMs on deterministic developer tasks including OCR, object detection, web scraping, speech-to-text, and classification.