LAUD: Integrating Large Language Models with Active Learning for Unlabeled Data

Tzu-Hsuan Chou, Chun-Nan Chou
11/18/2025
cs.LG

No confident code implementation found for this paper. 2 possible matches can be shown below.

Abstract

Large language models (LLMs) have shown a remarkable ability to generalize beyond their pre-training data, and fine-tuning LLMs can elevate performance to human-level and beyond. However, in real-world scenarios, lacking labeled data often prevents practitioners from obtaining well-performing models, thereby forcing practitioners to highly rely on prompt-based approaches that are often tedious, inefficient, and driven by trial and error. To alleviate this issue of lacking labeled data, we present a learning framework integrating LLMs with active learning for unlabeled dataset (LAUD). LAUD mitigates the cold-start problem by constructing an initial label set with zero-shot learning. Experimental results show that LLMs derived from LAUD outperform LLMs with zero-shot or few-shot learning on commodity name classification tasks, demonstrating the effectiveness of LAUD.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 2 weaker matches are hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

From Selection to Generation: A Survey of LLM-based Active Learning

86% match2025

Zero-to-Strong Generalization: Eliciting Strong Capabilities of Large Language Models Iteratively without Gold Labels

85% match2024

From Haystack to Needle: Label Space Reduction for Zero-shot Classification

85% match2025

Language Model-Driven Data Pruning Enables Efficient Active Learning

84% match2024

Towards Efficient Active Learning in NLP via Pretrained Representations

84% match2024

ActiveLLM: Large Language Model-based Active Learning for Textual Few-Shot Scenarios

83% match2024

Cite this paper

@article{chou2025laud,
  title  = {LAUD: Integrating Large Language Models with Active Learning for Unlabeled Data},
  author = {Tzu-Hsuan Chou and Chun-Nan Chou},
  year   = {2025},
  eprint = {2511.14738},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2511.14738v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2511.14738.svg)](https://codeofpaper.com/paper/2511.14738)

Discussion