ConceptRM: The Quest to Mitigate Alert Fatigue through Consensus-Based Purity-Driven Data Cleaning for Reflection Modelling
#ConceptRM #Alert Fatigue #Reflection Modeling #Data Cleaning #Intelligent Agents #Machine Learning #False Alert Filtering #Consensus-Based Learning
📌 Key Takeaways
- Researchers developed ConceptRM to combat alert fatigue in intelligent agent systems
- The method uses consensus-based data cleaning with minimal expert annotations
- ConceptRM outperforms existing models by up to 53.31% on in-domain datasets
- The approach significantly reduces annotation costs while improving alert filtering
- The research addresses the critical problem of users missing genuine alerts due to false positives
📖 Full Retelling
A team of researchers led by Yongda Yu and 11 co-authors introduced ConceptRM on February 9, 2026, a novel method to combat alert fatigue in intelligent agent systems through consensus-based data cleaning for reflection modeling. The research addresses the critical issue of users becoming desensitized to overwhelming volumes of false alerts generated by AI systems, which can cause them to overlook genuine problems. The paper, submitted to arXiv, proposes a solution that constructs high-quality training data for reflection models capable of filtering false alerts with minimal annotation cost. Alert fatigue represents a significant challenge in applications utilizing intelligent agents, where the sheer volume of notifications—predominantly false—can overwhelm human operators and lead to critical issues being missed. Current approaches typically involve training reflection models to filter these alerts using labeled data derived from user verification feedback. However, this data is often collected in production environments and contains substantial noise, making it unreliable for training effective models. Traditional methods of cleaning this noise through manual annotation are prohibitively expensive and time-consuming, creating a need for more efficient solutions. ConceptRM offers an innovative approach by utilizing only a small amount of expert annotations as anchors, then creating perturbed datasets with varying noise ratios. The method employs co-teaching to train multiple distinct models that engage in collaborative learning. By analyzing the consensus decisions of these models, the system can effectively identify reliable negative samples from a noisy dataset. Experimental results demonstrate that this approach significantly enhances the interception of false alerts while minimizing annotation costs, outperforming several state-of-the-art LLM baselines by up to 53.31% on in-domain datasets and 41.67% on out-of-domain datasets.
🏷️ Themes
Artificial Intelligence, Data Science, Human-Computer Interaction
📚 Related People & Topics
Data cleansing
Correcting inaccurate computer records
Data cleansing or data cleaning is the process of identifying and correcting (or removing) corrupt, inaccurate, or irrelevant records from a dataset, table, or database. It involves detecting incomplete, incorrect, or inaccurate parts of the data and then replacing, modifying, or deleting the affect...
Entity Intersection Graph
No entity connections available yet for this article.
Original Source
--> Computer Science > Computation and Language arXiv:2602.20166 [Submitted on 9 Feb 2026] Title: ConceptRM: The Quest to Mitigate Alert Fatigue through Consensus-Based Purity-Driven Data Cleaning for Reflection Modelling Authors: Yongda Yu , Lei Zhang , Xinxin Guo , Minghui Yu , Zhengqi Zhuang , Guoping Rong , Haifeng Shen , Zhengfeng Li , Boge Wang , Guoan Zhang , Bangyu Xiang , Xiaobin Xu View a PDF of the paper titled ConceptRM: The Quest to Mitigate Alert Fatigue through Consensus-Based Purity-Driven Data Cleaning for Reflection Modelling, by Yongda Yu and 11 other authors View PDF HTML Abstract: In many applications involving intelligent agents, the overwhelming volume of alerts (mostly false) generated by the agents may desensitize users and cause them to overlook critical issues, leading to the so-called ''alert fatigue''. A common strategy is to train a reflection model as a filter to intercept false alerts with labelled data collected from user verification feedback. However, a key challenge is the noisy nature of such data as it is often collected in production environments. As cleaning noise via manual annotation incurs high costs, this paper proposes a novel method ConceptRM for constructing a high-quality corpus to train a reflection model capable of effectively intercepting false alerts. With only a small amount of expert annotations as anchors, ConceptRM creates perturbed datasets with varying noise ratios and utilizes co-teaching to train multiple distinct models for collaborative learning. By analyzing the consensus decisions of these models, it effectively identifies reliable negative samples from a noisy dataset. Experimental results demonstrate that ConceptRM significantly enhances the interception of false alerts with minimal annotation cost, outperforming several state-of-the-art LLM baselines by up to 53.31% on in-domain datasets and 41.67% on out-of-domain datasets. Subjects: Computation and Language (cs.CL) ; Artificial Intelligence (cs.AI)...
Read full article at source