GLiClass: Generalist Lightweight Model for Sequence Classification Tasks
Authors: name: Ihor Stepanov; name: Mykhailo Shtopko; name: Dmytro Vodianytskyi; name: Oleksandr Lukashov; name: Alexander Yavorskyi; name: Mykyta Yaroshenko
Published: 2025-08-11 06:22:25

Abstract: Classification is one of the most widespread tasks in AI applications,
serving often as the first step in filtering, sorting, and categorizing data.
Since modern AI systems must handle large volumes of input data and early
pipeline stages can propagate errors downstream, achieving high efficiency and
accuracy is critical. Moreover, classification requirements can change
dynamically based on user needs, necessitating models with strong zero-shot
capabilities. While generative LLMs have become mainstream for zero-shot
classification due to their versatility, they suffer from inconsistent
instruction following and computational inefficiency. Cross-encoders, commonly
used as rerankers in RAG pipelines, face a different bottleneck: they must
process text-label pairs sequentially, significantly reducing efficiency with
large label sets. Embedding-based approaches offer good efficiency but struggle
with complex scenarios involving logical and semantic constraints. We propose
GLiClass, a novel method that adapts the GLiNER architecture for sequence
classification tasks. Our approach achieves strong accuracy and efficiency
comparable to embedding-based methods, while maintaining the flexibility needed
for zero-shot and few-shot learning scenarios. Additionally, we adapted
proximal policy optimization (PPO) for multi-label text classification,
enabling training classifiers in data-sparse conditions or from human feedback.