Papers with Machine learning models
Multi-Dimensional Gender Bias Classification (2020.emnlp-main)
Copied to clipboard
| Challenge: | a novel framework decomposes gender bias in text along several pragmatic and semantic dimensions . language is a primary means by which people communicate, express identities and categorize themselves . unwanted gender biases can affect downstream applications, leading to poor user experiences . |
| Approach: | They propose a framework that decomposes gender bias in text along several dimensions . they annotate eight large scale datasets with gender information and collect a benchmark . |
| Outcome: | The proposed framework decomposes gender bias in text along several pragmatic and semantic dimensions. |
StATIK: Structure and Text for Inductive Knowledge Graph Completion (2022.findings-naacl)
Copied to clipboard
Elan Markowitz, Keshav Balasubramanian, Mehrnoosh Mirtaheri, Murali Annavaram, Aram Galstyan, Greg Ver Steeg
| Challenge: | Knowledge graphs (KGs) represent incomplete knowledge bases. |
| Approach: | They propose to use language models to extract semantic information from text descriptions while using Message Passing Neural Networks to capture structural information. |
| Outcome: | The proposed model achieves state of the art on three challenging inductive baselines. |
WizMap: Scalable Interactive Visualization for Exploring Large Machine Learning Embeddings (2023.acl-demo)
Copied to clipboard
| Challenge: | Modern machine learning models learn latent embedding representations that capture the domain semantics of training data. |
| Approach: | They propose an interactive visualization tool to help users explore large embeddings by using a multi-resolution summarization method and a familiar map-like interface. |
| Outcome: | The proposed visualization tool scales to millions of embedding points directly in users’ web browsers and computational notebooks without the need for dedicated backend servers. |
BARLE: Background-Aware Representation Learning for Background Shift Out-of-Distribution Detection (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for OOD detection focus on identifying semantic-shift OOD samples . background-shift detection is more practical but challenging . |
| Approach: | They propose a background-aware representation learning approach for background-shift OOD detection in NLP. |
| Outcome: | The proposed method improves background-shift OOD detection while maintaining ID classification accuracy. |
Learning Label Modular Prompts for Text Classification in the Wild (2022.emnlp-main)
Copied to clipboard
| Challenge: | Recent advances in parameter efficient tuning of pretrained language models have limited performance. |
| Approach: | They propose a label-modular prompt tuning framework for text classification tasks that emulates the transient nature of real-world. |
| Outcome: | The proposed framework outperforms baselines in two formidable settings and shows strong generalisation ability. |
IMO: Greedy Layer-Wise Sparse Representation Learning for Out-of-Distribution Text Classification with Pre-trained Models (2024.acl-long)
Copied to clipboard
| Challenge: | IMO is a machine learning model that learns invariant features from unseen domains. |
| Approach: | They propose IMO: Invariant features Masks for Out-of-Distribution text classification to achieve OOD generalization by learning invariant feature masks. |
| Outcome: | The proposed model outperforms baseline models in various evaluation metrics and settings. |
Characterizing the Value of Information in Medical Notes (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Obtaining and analyzing information is critical for the diagnosis, prognosis, treatment, and prevention of disease. |
| Approach: | They propose a probing framework to select parts of notes that enable more accurate predictions than using all notes. |
| Outcome: | The proposed framework achieves better predictive performance with only 6.8% of all tokens for readmission prediction. |
Not to Overfit or Underfit the Source Domains? An Empirical Study of Domain Generalization in Question Answering (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to limit overfitting of training domains are rooted in this problem . domain generalization (DG) seeks to train models on a small number of source domains . |
| Approach: | They propose to use knowledge distillation to train models on a small number of source domains to maximize their zero-shot out-of-domain utility. |
| Outcome: | The proposed model learns its source domains better and has better out-of-domain generalization . the proposed model outperforms existing approaches that aim to limit overfitting . |
Using Adversarial Examples in Natural Language Processing (L18-1)
Copied to clipboard
| Challenge: | Recent advances in machine learning have led to the use of adversarial examples in training of neural networks. |
| Approach: | They investigate the effect of using adversarial examples during training of recurrent neural networks whose text input is in the form of a sequence of word/character embeddings. |
| Outcome: | The proposed method provides regularization effect and enables training of models with greater number of parameters without overfitting. |
Pruning Unsafe Tickets: A Resource-Efficient Framework for Safer and More Robust LLMs (2026.acl-long)
Copied to clipboard
| Challenge: | Empirical evaluations on ML models show substantial reductions in unsafe generations and improved robustness against jailbreak attacks. |
| Approach: | They propose a resource-efficient pruning framework that directly identifies unsafe behaviors while preserving model utility. |
| Outcome: | The proposed framework reduces unsafe generations and improves robustness against jailbreak attacks with minimal utility loss. |