Papers with detection systems
Granite Guardian: Comprehensive LLM Safeguarding (2025.naacl-industry)
Copied to clipboard
Inkit Padhi, Manish Nagireddy, Giandomenico Cornacchia, Subhajit Chaudhury, Tejaswini Pedapati, Pierre Dognin, Keerthiram Murugesan, Erik Miehling, Martín Santillán Cooper, Kieran Fraser, Giulio Zizzo, Muhammad Zaid Hameed, Mark Purcell, Michael Desmond, Qian Pan, Inge Vejsbjerg, Elizabeth M. Daly, Michael Hind, Werner Geyer, Ambrish Rawat, Kush R. Varshney, Prasanna Sattigeri
| Challenge: | a suite of advanced models is designed to detect and mitigate risks associated with prompts and responses. |
| Approach: | a team of researchers develop a model family to detect and mitigate risks associated with prompts and responses. the model family is based on the Granite 3.0 language models. |
| Outcome: | a new model family is designed to detect and mitigate risks associated with prompts and responses. |
Unleashing the Power of Discourse-Enhanced Transformers for Propaganda Detection (2024.eacl-long)
Copied to clipboard
| Challenge: | Existing systems focused on the surface words, ignoring the linguistic structure of the texts. |
| Approach: | They propose to use discourse analysis to analyze paragraph-level and token-level classifications and propose a Transformer architecture that can be used to detect propaganda. |
| Outcome: | The proposed system improves on English and Russian texts and shows strong correlations between propaganda instances and discourse spans. |
“Are you kidding me?”: Detecting Unpalatable Questions on Reddit (2021.eacl-main)
Copied to clipboard
| Challenge: | Existing methods to detect online abuse focus on the more explicit forms of abuse . existing methods focus on detecting subtler forms of online abuse leaving them unnoticed . |
| Approach: | They propose a task to detect unpalatable questions using reddit data to implement a context-aware dataset and implement 'learning models' they hope future research will address subtle forms of abuse since harm passes unnoticed through existing detection systems. |
| Outcome: | The proposed task is based on a dataset of reddit users and a conversational context. |
Improving Generalizability in Implicitly Abusive Language Detection with Concept Activation Vectors (2022.acl-long)
Copied to clipboard
| Challenge: | a new study shows that general abusive language classifiers are reliable in detecting explicit abuse but fail to detect more subtle abuses. |
| Approach: | They propose an interpretability technique to quantify the sensitivity of a trained model to new data . they propose a degree of explicitness metric to suggest out-of-domain unlabeled examples . |
| Outcome: | The proposed interpretability technique is useful for predicting the generalizability of the model on new data. |
Navigating the Shadows: Unveiling Effective Disturbances for Modern AI Content Detectors (2024.acl-long)
Copied to clipboard
| Challenge: | Recent research indicates that AI-text detection systems lack robustness and struggle to effectively differentiate perturbed texts. |
| Approach: | They propose to evaluate the robustness of current detection systems by using black-box text perturbation methods and adversarial learning experiments. |
| Outcome: | The proposed methods assess the robustness of current detection models across perturbation granularities and the impact of perturbation data augmentation on the robustity of AI-text detectors. |
LEAF: Towards Lightweight Explainable Hateful Video Detection via Self-Grounding CoT Guided Stage-Wise Distillation (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for detecting hateful videos rely on opaque models with no insight into their decisions. |
| Approach: | They propose a lightweight, explainable video detection framework that distills "explainability" from LMMs into efficient Smaller Multimodal Models (SMMs) they use a self-grounded chain-of-thought mechanism to generate unbiased supervision signals for videos . |
| Outcome: | The proposed framework outperforms existing methods in detection accuracy and explainability on three video benchmarks. |