Papers with learning frameworks
Detecting Denial-of-Service Attacks from Social Media Text: Applying NLP to Computer Security (N18-1)
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| Challenge: | Distributed Denial of Service (DDoS) attacks are becoming more frequent and more severe in their impact. |
| Approach: | They propose a feed-forward neural network and a partially labeled LDA model that use social media as an indirect measure of network service status. |
| Outcome: | The proposed model outperforms previous work by significant margins and provides the first fine-grained analysis of how the public reacts to ongoing network attacks. |
PICD-Instruct: A Generative Instruction Learning Framework for Few-Shot Multi-Intent Spoken Language Understanding (2025.findings-emnlp)
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| Challenge: | Recent advances in large language models (LLMs) have utilized instruction learning frameworks to model intent-slot interdependencies, typically requiring abundant data for effective training. |
| Approach: | They propose a generative framework based on Basic Instructions (BI), Pairwise Interaction Instructions and Contrastive Distinct Instructions to solve these challenges. |
| Outcome: | The proposed framework achieves state-of-the-art performance on public datasets. |
Unveiling Performance Challenges of Large Language Models in Low-Resource Healthcare: A Demographic Fairness Perspective (2025.coling-main)
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| Challenge: | Existing large language models (LLMs) are not effective in solving real-world healthcare tasks, but they are able to provide demographic information and provide biased health predictions. |
| Approach: | They evaluate state-of-the-art LLMs with three prevalent learning frameworks across six diverse healthcare tasks and find significant challenges in applying LLM to real-world healthcare tasks. |
| Outcome: | The proposed models perform poorly in real-world healthcare tasks and are inconsistent with existing learning frameworks. |
Low-Resource Grammatical Error Correction: Selective Data Augmentation with Round-Trip Machine Translation (2025.findings-acl)
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| Challenge: | Existing methods for grammatical error correction require large amounts of parallel training data. |
| Approach: | They propose to generate synthetic data through round-trip machine translation by generating a set of character-level errors using a technique known as SeLex-RT. |
| Outcome: | The proposed technique produces errors similar to those observed with language learners, but lacks gold-labeled training data. |