Papers by Almog Gueta
Knowledge is a Region in Weight Space for Fine-tuned Language Models (2023.findings-emnlp)
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| Challenge: | Specifically, language models that have been finetuned on the same dataset form a tight cluster in the weight space, while models finetuning on different datasets from the same underlying task form s looser clusters. |
| Approach: | They analyze the weight space and the underlying loss landscape of different models to determine how they are interconnected and how they perform. |
| Outcome: | The proposed method improves accuracy on 11 out of 12 datasets by starting from the center of the region, if not more, than using the pretrained model. |
Can LLMs Learn Macroeconomic Narratives from Social Media? (2025.findings-naacl)
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| Challenge: | Existing evaluation strategies for analyzing economic data with narratives are limited due to the complexity of the interplay of numerous factors and the difficulty in isolating causal relationships. |
| Approach: | They propose to use two Twitter datasets to capture economy-related narratives and use them to construct models using large language models. |
| Outcome: | The proposed models are able to predict macroeconomic fluctuations using the extracted or extracted narratives in two Twitter datasets. |
RefVNLI: Towards Scalable Evaluation of Subject-driven Text-to-image Generation (2025.findings-emnlp)
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Aviv Slobodkin, Hagai Taitelbaum, Yonatan Bitton, Brian Gordon, Michal Sokolik, Nitzan Bitton Guetta, Almog Gueta, Royi Rassin, Dani Lischinski, Idan Szpektor
| Challenge: | Existing methods assess only one aspect of the task, misalign with human judgments or rely on costly API-based evaluation. |
| Approach: | RefVNLI evaluates textual alignment and subject preservation in a single run. |
| Outcome: | RefVNLI outperforms or matches existing baselines across multiple benchmarks and subject categories. |