Papers with logistic regression
Resources and Experiments on Sentiment Classification for Georgian (2022.lrec-1)
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| Challenge: | a dataset for sentiment classification and semantic polarity dictionary for Georgian is available . a large number of linguistic resources are available for sentiment analysis for this language . |
| Approach: | They propose to create the first publicly available annotated dataset for sentiment classification and semantic polarity dictionary for Georgian. |
| Outcome: | The results are on par with state-of-the-art models for well-studied languages . the authors compare knowledge-and machine learning-based models to a well-supported language . |
Harry Potter and the Action Prediction Challenge from Natural Language (N19-1)
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| Challenge: | Using textual descriptions of scenes, we explore the challenge of action prediction from textual description. |
| Approach: | They propose a testbed to approximate whether text inference can be used to predict upcoming actions from textual descriptions of scenes. |
| Outcome: | The proposed model performs best for frequent actions and large scene descriptions, but logistic regression fails on infrequent actions. |
Knowing the Facts but Choosing the Shortcut: Understanding How Large Language Models Compare Entities (2026.eacl-long)
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| Challenge: | Large Language Models (LLMs) are increasingly used for knowledge-based reasoning tasks, yet understanding when they rely on genuine knowledge versus superficial heuristics remains challenging. |
| Approach: | They propose to ask LLMs to compare numerical attributes to find out which country has the highest population, France or Germany. |
| Outcome: | The proposed model comparisons show that heuristics override principled reasoning for larger models, while smaller models show no discrimination. |
Layer-wise Model Pruning based on Mutual Information (2021.emnlp-main)
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| Challenge: | In spite of impressive results of neural networks, the huge model size has hindered their applications in cases where computation and memory resources are limited. |
| Approach: | They propose a method for layer-wise pruning using mutual information based feature selection in SVMs and logistic regression. |
| Outcome: | The proposed pruning strategy offers greater speedup and higher performance than weight-based pruning methods. |
Generative or Discriminative? Revisiting Text Classification in the Era of Transformers (2025.emnlp-main)
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Siva Rajesh Kasa, Karan Gupta, Sumegh Roychowdhury, Ashutosh Kumar, Yaswanth Biruduraju, Santhosh Kumar Kasa, Pattisapu Nikhil Priyatam, Arindam Bhattacharya, Shailendra Agarwal, Vijay Huddar
| Challenge: | generative classifiers exhibit lower sample complexity but higher asymptotic error in simple linear settings, a trade-off that remains unexplored in the transformer era. |
| Approach: | They propose to evaluate generative and discriminative architectures for text classification using a generative model that learns the conditional probability distribution P (y|x) generative models are known to work better in low-data settings, giving rise to the classical 'two regimes' phenomenon for classification. |
| Outcome: | The proposed models show that the classical 'two regimes' manifests distinctly across different architectures and training paradigms. |
Automating Document Discovery in the Systematic Review Process: How to Use Chaff to Extract Wheat (L18-1)
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| Challenge: | Systematic reviews address research questions by comprehensively examining the entire published literature. |
| Approach: | They compare the impact of different schemes for choosing positive and negative examples from the different screening stages on the training of automated systems. |
| Outcome: | The proposed ranking system achieves an AUC of 0.803 and 0.768 when relying on gold standard decisions based on title and abstracts of articles, and an AUT of 0.625 and 0.839 when based upon gold standard decision based in full text. |
Analyzing Vocabulary Commonality Index Using Large-scaled Database of Child Language Development (L18-1)
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| Challenge: | a vocabulary commonality index is used to investigate to what extent each child acquires common words during the early stages of lexical development. |
| Approach: | They propose a vocabulary commonality index to investigate to what extent each child acquires common words during the early stages of lexical development. |
| Outcome: | The proposed index can be used to understand to what extent each child acquires common words during the early stages of lexical development. |
Dementia Through Different Eyes: Explainable Modeling of Human and LLM Perceptions for Early Awareness (2025.findings-emnlp)
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| Challenge: | a new study examines how dementia is perceived by non-experts . human perception of dementia is inconsistent and relies on a narrow set of cues compared to LLMs based on broader clinical patterns . |
| Approach: | They propose a method that uses LLMs to extract high-level, expert-guided features . human perception of dementia is inconsistent and relies on a narrow set of cues, they say . |
| Outcome: | The proposed method analyzes picture descriptions to assess whether they were produced by non-experts or by nonexperts. |
Dimensions of Online Conflict: Towards Modeling Agonism (2023.findings-emnlp)
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Matt Canute, Mali Jin, Hannah Holtzclaw, Alberto Lusoli, Philippa Adams, Mugdha Pandya, Maite Taboada, Diana Maynard, Wendy Hui Kyong Chun
| Challenge: | agonism fosters robust discussions, but hateful antagonism undermines constructive dialogue . a new study analyzes Twitter conversations to identify different dimensions of conflict . |
| Approach: | They annotated Twitter conversations related to trending controversial topics to model conflict on a richly annotized dataset. |
| Outcome: | The proposed model can help to moderate online conflicts and improve content monetization. |
AdaSteer: Your Aligned LLM is Inherently an Adaptive Jailbreak Defender (2025.emnlp-main)
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Weixiang Zhao, Jiahe Guo, Yulin Hu, Yang Deng, An Zhang, Xingyu Sui, Xinyang Han, Yanyan Zhao, Bing Qin, Tat-Seng Chua, Ting Liu
| Challenge: | Activation steering offers training-free defense but relies on fixed steering coefficients, resulting in suboptimal protection and increased false rejections of benign inputs. |
| Approach: | They propose an adaptive activation steering method that dynamically adjusts model behavior based on input characteristics. |
| Outcome: | The proposed method outperforms baseline methods across multiple jailbreak attacks with minimal impact on utility. |