• Computer Science > Computation and Language [Submitted on 9 Feb 2026] Title:ConceptRM: The Quest to Mitigate Alert Fatigue through Consensus-Based Purity-Driven Data Cleaning for Reflection Modelling View PDF HTML (experimental)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.

Article Summaries:

  • Computer Science > Computation and Language [Submitted on 9 Feb 2026] Title:ConceptRM: The Quest to Mitigate Alert Fatigue through Consensus-Based Purity-Driven Data Cleaning for Reflection Modelling View PDF HTML (experimental)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 ver

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