ACT as Human: Multimodal Large Language Model Data Annotation with Critical Thinking

• URL: http://arxiv.org/abs/2511.09833v1
• Date: Fri, 14 Nov 2025 01:12:06 GMT
• Title: ACT as Human: Multimodal Large Language Model Data Annotation with Critical Thinking
• Authors: Lequan Lin, Dai Shi, Andi Han, Feng Chen, Qiuzheng Chen, Jiawen Li, Zhaoyang Li, Jiyuan Li, Zhenbang Sun, Junbin Gao,
• Abstract summary: Supervised learning relies on high-quality labeled data, but obtaining such data through human annotation is both expensive and time-consuming.<n>Recent work explores using large language models (LLMs) for annotation, but LLM-generated labels still fall short of human-level quality.<n>To address this problem, we propose the Supervise with Critical Thinking (ACT) data pipeline.
• Score: 44.58919028628059
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Supervised learning relies on high-quality labeled data, but obtaining such data through human annotation is both expensive and time-consuming. Recent work explores using large language models (LLMs) for annotation, but LLM-generated labels still fall short of human-level quality. To address this problem, we propose the Annotation with Critical Thinking (ACT) data pipeline, where LLMs serve not only as annotators but also as judges to critically identify potential errors. Human effort is then directed towards reviewing only the most "suspicious" cases, significantly improving the human annotation efficiency. Our major contributions are as follows: (1) ACT is applicable to a wide range of domains, including natural language processing (NLP), computer vision (CV), and multimodal understanding, by leveraging multimodal-LLMs (MLLMs). (2) Through empirical studies, we derive 7 insights on how to enhance annotation quality while efficiently reducing the human cost, and then translate these findings into user-friendly guidelines. (3) We theoretically analyze how to modify the loss function so that models trained on ACT data achieve similar performance to those trained on fully human-annotated data. Our experiments show that the performance gap can be reduced to less than 2% on most benchmark datasets while saving up to 90% of human costs.

Related papers

• Are Multimodal Large Language Models Good Annotators for Image Tagging? [62.01475514488922]
  This paper aims to analyze the gap between MLLM-generated and human annotations.<n>We propose TagLLM, a novel framework for image tagging, which aims to narrow the gap between MLLM-generated and human annotations.
  arXiv  Detail & Related papers  (2026-02-24T14:53:16Z)
• Evaluating Large Language Models as Expert Annotators [17.06186816803593]
  This paper investigates whether top-performing language models can serve as direct alternatives to human expert annotators.<n>We evaluate both individual LLMs and multi-agent approaches across three highly specialized domains: finance, biomedicine, and law.<n>Our empirical results reveal that individual LLMs equipped with inference-time techniques show only marginal or even negative performance gains.
  arXiv  Detail & Related papers  (2025-08-11T10:19:10Z)
• Can Reasoning Help Large Language Models Capture Human Annotator Disagreement? [84.32752330104775]
  Variation in human annotation (i.e., disagreements) is common in NLP.<n>We evaluate the influence of different reasoning settings on Large Language Model disagreement modeling.<n>Surprisingly, our results show that RLVR-style reasoning degrades performance in disagreement modeling.
  arXiv  Detail & Related papers  (2025-06-24T09:49:26Z)
• Verifying the Verifiers: Unveiling Pitfalls and Potentials in Fact Verifiers [59.168391398830515]
  We evaluate 12 pre-trained LLMs and one specialized fact-verifier, using a collection of examples from 14 fact-checking benchmarks.<n>We highlight the importance of addressing annotation errors and ambiguity in datasets.<n> frontier LLMs with few-shot in-context examples, often overlooked in previous works, achieve top-tier performance.
  arXiv  Detail & Related papers  (2025-06-16T10:32:10Z)
• Evaluating LLM-Contaminated Crowdsourcing Data Without Ground Truth [18.069595635842557]
  Large language models (LLMs) by crowdsourcing workers pose a challenge to datasets intended to reflect human input.<n>We propose a training-free scoring mechanism with theoretical guarantees under a crowdsourcing model that accounts for LLM collusion.
  arXiv  Detail & Related papers  (2025-06-08T04:38:39Z)
• Truth in the Few: High-Value Data Selection for Efficient Multi-Modal Reasoning [71.3533541927459]
  We propose a novel data selection paradigm termed Activation Reasoning Potential (RAP)<n>RAP identifies cognitive samples by estimating each sample's potential to stimulate genuine multi-modal reasoning.<n>Our RAP method consistently achieves superior performance using only 9.3% of the training data, while reducing computational costs by over 43%.
  arXiv  Detail & Related papers  (2025-06-05T08:40:24Z)
• Illuminating Blind Spots of Language Models with Targeted Agent-in-the-Loop Synthetic Data [9.982616173090264]
  Language models (LMs) have achieved impressive accuracy across a variety of tasks but remain vulnerable to high-confidence misclassifications (UUs) UUs cluster into blind spots in the feature space, leading to significant risks in high-stakes applications. We propose a novel approach to address blind spot mitigation through the use of intelligent agents as teachers to characterize UU-type errors.
  arXiv  Detail & Related papers  (2024-03-26T16:49:25Z)
• Interactive Multi-fidelity Learning for Cost-effective Adaptation of Language Model with Sparse Human Supervision [6.151133144093847]
  Large language models (LLMs) have demonstrated remarkable capabilities in various tasks. We propose a novel Interactive Multi-Fidelity Learning (IMFL) framework for the cost-effective development of small domain-specific LMs.
  arXiv  Detail & Related papers  (2023-10-31T03:39:23Z)
• LLMaAA: Making Large Language Models as Active Annotators [32.57011151031332]
  We propose LLMaAA, which takes large language models as annotators and puts them into an active learning loop to determine what to annotate efficiently. We conduct experiments and analysis on two classic NLP tasks, named entity recognition and relation extraction. With LLMaAA, task-specific models trained from LLM-generated labels can outperform the teacher within only hundreds of annotated examples.
  arXiv  Detail & Related papers  (2023-10-30T14:54:15Z)
• CoAnnotating: Uncertainty-Guided Work Allocation between Human and Large Language Models for Data Annotation [94.59630161324013]
  We propose CoAnnotating, a novel paradigm for Human-LLM co-annotation of unstructured texts at scale. Our empirical study shows CoAnnotating to be an effective means to allocate work from results on different datasets, with up to 21% performance improvement over random baseline.
  arXiv  Detail & Related papers  (2023-10-24T08:56:49Z)
• Benchmarking Large Language Models for News Summarization [79.37850439866938]
  Large language models (LLMs) have shown promise for automatic summarization but the reasons behind their successes are poorly understood. We find instruction tuning, and not model size, is the key to the LLM's zero-shot summarization capability.
  arXiv  Detail & Related papers  (2023-01-31T18:46:19Z)

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.