Papers with anomaly detection
‘Am I the Bad One’? Predicting the Moral Judgement of the Crowd Using Pre–trained Language Models (2022.lrec-1)
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| Challenge: | Existing studies on NLP touch upon moral contexts in text. |
| Approach: | They construct a dataset that can be used for moral judgement tasks on a popular reddit subreddit. |
| Outcome: | The proposed model passes moral judgements on posts from a popular reddit subreddit . it shows that the model can be fine tuned and improves across the datasets . |
On the True Distribution Approximation of Minimum Bayes-Risk Decoding (2024.naacl-short)
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| Challenge: | Minimum Bayes-risk (MBR) decoding has recently gained renewed attention in text generation. |
| Approach: | They propose to use anomaly detection to measure the degree of approximation by sampling texts from a model and selecting the text with the highest similarity to the others. |
| Outcome: | The proposed method shows that previous hypotheses about samples do not correlate well with the variation, but the results support the core assumption of MBR decoding. |
Treating Dialogue Quality Evaluation as an Anomaly Detection Problem (2020.lrec-1)
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| Challenge: | Dialogue systems for interaction with humans are becoming more popular . the best way to estimate their success is through means of human evaluation . |
| Approach: | They investigate the effectiveness of perceiving dialogue evaluation as an anomaly detection task. |
| Outcome: | The proposed approach is based on four models and shows negative results . the proposed approach could be used in the future to improve human-led dialogue evaluations. |
VANE-Bench: Video Anomaly Evaluation Benchmark for Conversational LMMs (2025.findings-naacl)
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| Challenge: | Large Language Models (LLMs) have greatly influenced the development of Large Multi-modal Video Models. |
| Approach: | They propose a benchmark to assess the proficiency of Large Multi-modal Video Models (LMMs) in detecting and localizing anomalies and inconsistencies in videos. |
| Outcome: | The proposed benchmark assesses the proficiency of Video-LMMs in detecting and localizing anomalies and inconsistencies in videos. |
Reap the Wild Wind: Detecting Media Storms in Large-Scale News Corpora (2024.findings-emnlp)
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| Challenge: | Despite their importance, there has been little systematic and empirical research on media storms due to issues of measurement and operationalization. |
| Approach: | They propose an iterative method to identify media storms in a large-scale corpus of news articles. |
| Outcome: | The proposed method can identify media storms in a large-scale corpus of news articles. |
Integrating Data Validation with Large Language Models for Regulation-Guided Tabular Anomaly Detection (2026.acl-long)
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| Challenge: | Existing tabular anomaly detection methods focus on detecting anomalies based on data distribution without considering regulatory compliance. |
| Approach: | They propose a task that leverages regulations to detect anomalies in tabular data . they also develop three new datasets to address this task . |
| Outcome: | The proposed method outperforms baselines on three new datasets. |
Dysarthric speech evaluation: automatic and perceptual approaches (L18-1)
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| Challenge: | Perceptual evaluation is still the most common method in clinical practice for the diagnosis and monitoring of the condition progression of people suffering from dysarthria. |
| Approach: | They propose an automatic approach for anomaly detection at the phone level for dysarthric speech . they propose a perceptual evaluation protocol that uses annotated french corpora to analyze the system behavior. |
| Outcome: | The proposed method was validated on different corpora and speech styles. |
EMO&LY (EMOtion and AnomaLY) : A new corpus for anomaly detection in an audiovisual stream with emotional context. (L18-1)
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| Challenge: | Anomalies in discourse are induced or acted by a machine learning algorithm. |
| Approach: | They propose to use facial and speech video to create a corpus that contains controlled anomalies. |
| Outcome: | The proposed corpus contains controlled anomalies in speech and facial video recordings of subjects. |
An Empirical Investigation of Contextualized Number Prediction (2020.emnlp-main)
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| Challenge: | a large scale empirical investigation of contextualized number prediction in running text is needed. |
| Approach: | They propose a suite of output distribution parameterizations that incorporate latent variables to add expressivity and better fit the natural distribution of numeric values in running text. |
| Outcome: | The proposed models outperform flow-based models on two numeric datasets in the financial and scientific domain. |
Time-RA: Towards Time Series Reasoning for Anomaly Diagnosis with LLM Feedback (2026.findings-acl)
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Yiyuan Yang, Zichuan Liu, Lei Song, Kai Ying, Stephen Wang, Joshua Thomas Bamford, Svitlana Vyetrenko, Jiang Bian, Qingsong Wen
| Challenge: | Time series anomaly detection (TSAD) has traditionally focused on binary classification and lacks the fine-grained categorization and explanatory reasoning required for transparent decision-making. |
| Approach: | They propose a time-series reasoning task that reformulates TSAD from discriminative to reasoning-intensive paradigm. |
| Outcome: | The proposed task reformulates TSAD from discriminative to reasoning-intensive paradigm. |
Confront Insider Threat: Precise Anomaly Detection in Behavior Logs Based on LLM Fine-Tuning (2025.coling-main)
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| Challenge: | Current methods for insider threat detection suffer from low precision and information loss . a novel approach to detect insider threats is needed to improve accuracy . |
| Approach: | They propose a precise anomaly detection solution based on Large Language Model (LLM) fine-tuning . they represent user behavior in natural language and implement a threat tracing mechanism . |
| Outcome: | The proposed solution achieves an F1 score of 0.8941 on the CERT v6.2 dataset . |
Improving Robustness of GNN-based Anomaly Detection by Graph Adversarial Training (2024.lrec-main)
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| Challenge: | Graph neural networks excel at anomaly detection, but exhibit vulnerability to attacks . novel mechanism for graph adversarial training designed to bolster anomaly detectors . |
| Approach: | They propose a mechanism for graph adversarial training to bolster anomaly detection systems against potential poisoning attacks. |
| Outcome: | The proposed method bolsters GNN-based anomaly detection systems against poisoning attacks. |
Enhancing Two Steps Textual Anomaly Detection through Anisotropy Mitigation (2026.acl-long)
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| Challenge: | Recent approaches to anomaly detection focus on embeddings from pre-trained models . however, the geometric properties of pre-training embedders can hinder detection algorithms . |
| Approach: | They propose to apply anomaly detection algorithms to embeddings from pre-trained models to improve accuracy. |
| Outcome: | The proposed approach improves similarity-trained models by adapting embeddings to assumptions made by classical detection algorithms. |
CAVE : Detecting and Explaining Commonsense Anomalies in Visual Environments (2025.emnlp-main)
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Rishika Bhagwatkar, Syrielle Montariol, Angelika Romanou, Beatriz Borges, Irina Rish, Antoine Bosselut
| Challenge: | a new benchmark for computer vision fails to capture richness and unpredictability of real-world anomalies . state-of-the-art VLMs struggle with visual anomaly perception and commonsense reasoning . elucidating the nature of anomalies is a fundamental human trait . |
| Approach: | They propose a benchmark for visual anomalies that includes annotations for visual grounding and categorizing anomalies based on their visual manifestations, their complexity, severity, and commonness. |
| Outcome: | The proposed benchmark improves on existing vision models by incorporating visual annotations. |
Time-MQA: Time Series Multi-Task Question Answering with Context Enhancement (2025.acl-long)
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Yaxuan Kong, Yiyuan Yang, Yoontae Hwang, Wenjie Du, Stefan Zohren, Zhangyang Wang, Ming Jin, Qingsong Wen
| Challenge: | Existing time series models focus on a narrow spectrum of tasks, such as forecasting or anomaly detection. |
| Approach: | They propose a framework that enables natural language queries across multiple time series tasks such as numerical analytical tasks and open-ended question answering with reasoning. |
| Outcome: | The proposed framework enables natural language queries across multiple time series tasks and allows for more advanced and intuitive interactions with temporal data. |
CMHKF: Cross-Modality Heterogeneous Knowledge Fusion for Weakly Supervised Video Anomaly Detection (2025.acl-long)
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| Challenge: | Existing methods focus mainly on visual modalities, neglecting rich multi-modality information. |
| Approach: | They propose a framework that integrates cross-modality knowledge from video, audio and text to improve anomaly detection and localization. |
| Outcome: | The proposed framework improves detection and localization of anomalies using video-level labels. |