Next Generation Active Learning: Mixture of LLMs in the Loop

• URL: http://arxiv.org/abs/2601.15773v1
• Date: Thu, 22 Jan 2026 09:01:42 GMT
• Title: Next Generation Active Learning: Mixture of LLMs in the Loop
• Abstract summary: We propose a novel active learning framework, replacing human annotators with labels generated through a Mixture-of-LLMs-based annotation model.<n>Our framework is built on lightweight LLMs, enabling it to operate fully on local machines in real-world applications.
• Score: 7.786330678327967
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the rapid advancement and strong generalization capabilities of large language models (LLMs), they have been increasingly incorporated into the active learning pipelines as annotators to reduce annotation costs. However, considering the annotation quality, labels generated by LLMs often fall short of real-world applicability. To address this, we propose a novel active learning framework, Mixture of LLMs in the Loop Active Learning, replacing human annotators with labels generated through a Mixture-of-LLMs-based annotation model, aimed at enhancing LLM-based annotation robustness by aggregating the strengths of multiple LLMs. To further mitigate the impact of the noisy labels, we introduce annotation discrepancy and negative learning to identify the unreliable annotations and enhance learning effectiveness. Extensive experiments demonstrate that our framework achieves performance comparable to human annotation and consistently outperforms single-LLM baselines and other LLM-ensemble-based approaches. Moreover, our framework is built on lightweight LLMs, enabling it to operate fully on local machines in real-world applications.

Related papers

• De-attribute to Forget for LLM Unlearning [79.82535763351594]
  The rapid development of large language models (LLMs) has raised concerns on the use of inappropriate data for training.<n>This paper proposes the first LLM unlearning framework based on data attribution rewards called DareU.
  arXiv  Detail & Related papers  (2026-05-29T07:03:20Z)
• Beyond Static LLM Policies: Imitation-Enhanced Reinforcement Learning for Recommendation [23.945049006150555]
  Large language models (LLMs) have become critical tools for enhancing user engagement by delivering personalized content across diverse digital platforms.<n>Direct deployment of LLMs as primary recommendation policies presents notable challenges, including persistent latency issues.<n>This paper proposes a novel offline reinforcement learning framework that leverages imitation learning from LLM-generated trajectories.
  arXiv  Detail & Related papers  (2025-10-15T07:28:29Z)
• Just Put a Human in the Loop? Investigating LLM-Assisted Annotation for Subjective Tasks [18.695435335031355]
  In subjective annotation tasks with multiple plausible answers, reviewing LLM outputs can change the label distribution.<n>We conducted a pre-registered experiment with 410 unique annotators and over 7,000 annotations.<n>We find that presenting crowdworkers with LLM-generated annotation suggestions did not make them faster, but did improve their self-reported confidence in the task.
  arXiv  Detail & Related papers  (2025-07-21T17:29:21Z)
• Enhancing LLM-based Recommendation through Semantic-Aligned Collaborative Knowledge [25.757451106327167]
  SeLLa-Rec focuses on achieving alignment between the semantic spaces of Collabs. and LLMs.<n>This alignment fosters effective knowledge fusion, mitigating the influence of discriminative noise.<n> Experiments conducted on two public benchmark datasets demonstrate that SeLLa-Rec achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-04-14T11:15:30Z)
• Utility-Focused LLM Annotation for Retrieval and Retrieval-Augmented Generation [96.18720164390699]
  This paper explores the use of large language models (LLMs) for annotating document utility in training retrieval and retrieval-augmented generation (RAG) systems.<n>Our results show that LLM-generated annotations enhance out-of-domain retrieval performance and improve RAG outcomes compared to models trained solely on human annotations or downstream QA metrics.
  arXiv  Detail & Related papers  (2025-04-07T16:05:52Z)
• LLM-Forest: Ensemble Learning of LLMs with Graph-Augmented Prompts for Data Imputation [50.375567142250446]
  Large language models (LLMs), trained on vast corpora, have shown strong potential in data generation.<n>We propose a novel framework, LLM-Forest, which introduces a "forest" of few-shot prompt learning LLM "trees" with their outputs aggregated via confidence-based weighted voting.<n>This framework is established on a new concept of bipartite information graphs to identify high-quality relevant neighboring entries with both feature and value granularity.
  arXiv  Detail & Related papers  (2024-10-28T20:42:46Z)
• CoMMIT: Coordinated Multimodal Instruction Tuning [90.1532838391285]
  Multimodal large language models (MLLMs) generally involve cooperative learning between a backbone LLM and a feature encoder of non-text input modalities.<n>In this paper, we analyze the MLLM instruction tuning from both theoretical and empirical perspectives.<n>We propose a Multimodal Balance Coefficient that enables quantitative measurement of the balance of learning.
  arXiv  Detail & Related papers  (2024-07-29T23:18:55Z)
• One Token Can Help! Learning Scalable and Pluggable Virtual Tokens for Retrieval-Augmented Large Language Models [67.49462724595445]
  Retrieval-augmented generation (RAG) is a promising way to improve large language models (LLMs)<n>We propose a novel method that involves learning scalable and pluggable virtual tokens for RAG.
  arXiv  Detail & Related papers  (2024-05-30T03:44:54Z)
• Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing [56.75702900542643]
  We introduce AlphaLLM for the self-improvements of Large Language Models.<n>It integrates Monte Carlo Tree Search (MCTS) with LLMs to establish a self-improving loop.<n>Our experimental results show that AlphaLLM significantly enhances the performance of LLMs without additional annotations.
  arXiv  Detail & Related papers  (2024-04-18T15:21:34Z)
• Supervised Knowledge Makes Large Language Models Better In-context Learners [94.89301696512776]
  Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering. The challenge of improving the generalizability and factuality of LLMs in natural language understanding and question answering remains under-explored. We propose a framework that enhances the reliability of LLMs as it: 1) generalizes out-of-distribution data, 2) elucidates how LLMs benefit from discriminative models, and 3) minimizes hallucinations in generative tasks.
  arXiv  Detail & Related papers  (2023-12-26T07:24:46Z)
• FreeAL: Towards Human-Free Active Learning in the Era of Large Language Models [21.88032973150393]
  FreeAL interactively distills and filters task-specific knowledge from large language models (LLMs) Experiments on eight benchmark datasets demonstrate that FreeAL largely enhances the zero-shot performances for both SLM and LLM without any human supervision.
  arXiv  Detail & Related papers  (2023-11-27T08:23:08Z)
• On Learning to Summarize with Large Language Models as References [101.79795027550959]
  Large language models (LLMs) are favored by human annotators over the original reference summaries in commonly used summarization datasets. We study an LLM-as-reference learning setting for smaller text summarization models to investigate whether their performance can be substantially improved.
  arXiv  Detail & Related papers  (2023-05-23T16:56:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.