Papers with model agnostic
Knowledge Router: Learning Disentangled Representations for Knowledge Graphs (2021.naacl-main)
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| Challenge: | Existing approaches to learning from relational patterns and structural information ignore the intrinsic complexity of KGs. |
| Approach: | They propose to learn latent properties of KG entities by using a neighborhood mechanism to disentangle the inner properties of each entity. |
| Outcome: | The proposed method significantly improves performance on key metrics on several benchmark datasets. |
Beyond Reptile: Meta-Learned Dot-Product Maximization between Gradients for Improved Single-Task Regularization (2021.findings-emnlp)
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| Challenge: | Existing approaches to improve generalization of neural models use a small component of the gradient for maximizing dot-product between batches. |
| Approach: | They propose to use a finite differences first-order algorithm to calculate a gradient from dot-product of gradients and regularize it. |
| Outcome: | The proposed method outperforms previous approaches of Reptile and MAML when used as a regularization technique. |
Differentially Private Next-Token Prediction of Large Language Models (2024.naacl-long)
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| Challenge: | Large language models (LLMs) are becoming increasingly important for ensuring privacy, but DP-SGD overestimates an adversary’s capabilities in having white box access. |
| Approach: | They propose a private prediction protocol for next-token prediction that utilizes the inherent stochasticity of next-toning and a public model to achieve Differential Privacy. |
| Outcome: | The proposed protocol outperforms DP-SGD and DP training methods for privacy on large datasets. |
LLMs Enable Bag-of-Texts Representations for Short-Text Clustering (2026.acl-long)
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| Challenge: | Existing methods for short text clustering require labeling and no embeddings optimization. |
| Approach: | They propose a training-free method for unsupervised short text clustering that relies less on careful selection of embedders than other methods. |
| Outcome: | The proposed method achieves comparable or superior results to state-of-the-art methods, but without embeddings optimization or prior knowledge of clusters or labels. |
A Deep Transfer Learning Method for Cross-Lingual Natural Language Inference (2022.lrec-1)
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| Challenge: | Natural Language Inference (NLI) is a crucial task in AI and natural language processing. |
| Approach: | They propose an effective transfer learning approach for cross-lingual NLI . they perform experiments on English-Hindi language pairs in cross-linguistic setting . |
| Outcome: | The proposed model improves the baseline model by 10% over the state-of-the-art model. |