Papers by Ido Guy
Cross-Cultural Transfer Learning for Text Classification (D19-1)
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| Challenge: | a large dataset is required to achieve competitive performance in most natural language tasks. large datasets are expensive, time consuming, and error-prone. |
| Approach: | They propose a transfer-learning framework that leverages bilingual corpora for natural language text classification using no task-specific data. |
| Outcome: | The proposed framework can achieve good performance on formality classification and sarcasm detection tasks without any task-specific labeled data. |
Propaganda Signals in LLMs: Perspectival Divergence and Narrative Framing in the Russia-Ukraine War (2026.findings-acl)
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| Challenge: | Large Language Models are increasingly used to explain, summarize, and translate real-world events . a recent study examined whether LLMs reproduce conflict-specific propaganda . |
| Approach: | They evaluate LLMs under several prompting contexts to determine which side they are closer to . they find model-specific leanings and technique profiles that persist across prompts . |
| Outcome: | The proposed model outputs align with competing narratives from different information ecosystems. |
User-Centric Evidence Ranking for Attribution and Fact Verification (2026.eacl-long)
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| Challenge: | Large language models often present users with insufficient or redundant information, leading to inefficient and error-prone verification. |
| Approach: | They propose a task that prioritizes presenting sufficient information as early as possible in a ranked list. |
| Outcome: | The proposed task minimizes user reading effort while making all available evidence accessible for sequential verification. |