Papers by Ido Cohen
Small Models, Big Results: Achieving Superior Intent Extraction through Decomposition (2025.emnlp-main)
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Danielle Cohen, Yoni Halpern, Noam Kahlon, Joel Oren, Omri Berkovitch, Sapir Caduri, Ido Dagan, Anatoly Efros
| Challenge: | Large multi-modal large language models are good at extracting user intents from UI sequences, but smaller models struggle with accurate intent inference. |
| Approach: | They propose a decomposed approach for extracting user intent from small models . they perform structured interaction summarization and intent extraction using a fine-tuned model . |
| Outcome: | The proposed method surpasses the performance of large MLLMs in the intent extraction task. |
Performance Gap in Entity Knowledge Extraction Across Modalities in Vision Language Models (2025.acl-long)
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| Challenge: | Vision-language models excel at extracting and reasoning about information from images, yet their capacity to leverage internal knowledge about specific entities remains underexplored. |
| Approach: | They propose a dataset which allows separating entity recognition and question answering . they hypothesize that this decline arises from limitations in how information flows from image tokens to query tokens. |
| Outcome: | The proposed model performance drops when the entity is presented visually rather than textually. |
Understanding Transformer Memorization Recall Through Idioms (2023.eacl-main)
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| Challenge: | Existing methods for analyzing memorization use definitions that are based on model performance, which changes between models and often also between training runs. |
| Approach: | They propose idioms as inputs that typically trigger memory recall and propose a set of English idiomas to test their methodological framework for probing and characterizing recall of memorized sequences in transformer LMs. |
| Outcome: | The proposed framework compares model behavior on memorized vs. non-memorized inputs across different model sizes and architectures. |