Papers by Itzik Malkiel
Caption Enriched Samples for Improving Hateful Memes Detection (2021.emnlp-main)
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| Challenge: | Existing methods for classifying memes are difficult to perform, with human accuracy only about 85% . recent state-of-the-art models perform considerably less accurately, achieving up to 64.73% accuracy. |
| Approach: | They propose to use an off-the-shelf caption generator to capture the first image and overlayed text. |
| Outcome: | The proposed tool improves classification accuracy for unimodal and multimodal models . the proposed tool can be used to model the contrast between image content and overlayed text . |
Self-Supervised Document Similarity Ranking via Contextualized Language Models and Hierarchical Inference (2021.findings-acl)
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| Challenge: | Existing approaches to document-to-document similarity ranking are limited to relatively short documents or lack similarity labels. |
| Approach: | They propose a self-supervised method for document similarity ranking that can be applied to documents of arbitrary length. |
| Outcome: | The proposed model outperforms existing methods on large documents datasets. |
RecoBERT: A Catalog Language Model for Text-Based Recommendations (2020.findings-emnlp)
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| Challenge: | RecoBERT is a BERT-based approach for learning catalog-specialized language models for text-based item recommendations. |
| Approach: | They propose a BERT-based approach for learning catalog-specialized language models for text-based item recommendations that incorporates four scores during inference. |
| Outcome: | The proposed model can infer item-to-item similarities more accurately than other methods. |
InterrogateLLM: Zero-Resource Hallucination Detection in LLM-Generated Answers (2024.acl-long)
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| Challenge: | Existing methods for detecting hallucinations in large language models are limited due to their high frequency and high accuracy. |
| Approach: | They propose a method to detect hallucinations in large language models by repeating model-generated responses from its generated answer. |
| Outcome: | The proposed method achieves 87% hallucinations in a specific experiment without external knowledge. |
Maximal Multiverse Learning for Promoting Cross-Task Generalization of Fine-Tuned Language Models (2021.eacl-main)
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| Challenge: | Recent studies suggest the use of general language models for improving natural language processing tasks. |
| Approach: | They propose a method that leverages the second phase to its fullest by applying an extensive number of parallel classifier heads, which are enforced to be orthogonal, while adaptively eliminating the weaker heads during training. |
| Outcome: | The proposed method improves the generalization ability of BERT, sometimes leading to a +9% gain in accuracy. |
MTAdam: Automatic Balancing of Multiple Training Loss Terms (2021.emnlp-main)
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| Challenge: | In supervised and unsupervised learning, adding loss terms often leads to improved performance. |
| Approach: | They propose an algorithm that balances the gradient magnitude of loss terms across all layers . they use Adam to add loss terms to neural models, but add more terms as they are added . |
| Outcome: | The proposed method improves performance and improves training outcomes. |