Papers by Emilio Ferrara
Can LLMs Express Personality Across Cultures? Introducing CulturalPersonas for Evaluating Trait Alignment (2025.findings-emnlp)
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| Challenge: | Recent studies have explored personality evaluation of LLMs, but they largely overlook the interplay between culture and personality. |
| Approach: | They propose a large-scale benchmark for evaluating LLMs’ personality expression in culturally grounded, behaviorally rich contexts. |
| Outcome: | The proposed benchmark improves alignment with country-specific human personality distributions and elicits more expressive, culturally coherent outputs compared to existing benchmarks. |
SilentDrift: Exploiting Action Chunking for Stealthy Backdoor Attacks on Vision-Language-Action Models (2026.findings-acl)
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| Challenge: | Existing backdoor attacks introduce kinematic discontinuities and distributional anomalies that can be flagged by standard trajectory detection. |
| Approach: | They propose a backdoor attack exploiting an intra-chunk visual open-loop vulnerability . they propose 93.2% Attack Success Rate and a poisoning rate under 2% . |
| Outcome: | The proposed attack achieves a 93.2% Attack Success Rate with a poisoning rate under 2% while maintaining a 95.3% Clean Task Success Rate. |
Psychological Steering in LLMs: An Evaluation of Effectiveness and Trustworthiness (2026.acl-long)
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Amin Banayeeanzade, Ala N. Tak, Fatemeh Bahrani, Anahita Bolourani, Leonardo Blas, Emilio Ferrara, Jonathan Gratch, Sai Praneeth Karimireddy
| Challenge: | Using a model with a high degree of emotion and personality control, large language models can be used to control socially interactive interactions. |
| Approach: | They propose a Psychologically-informed benchmark to evaluate LLM steering effectiveness and trustworthiness across emotion and personality domains. |
| Outcome: | The framework establishes the first holistic evaluation of emotion and personality steering, offering insights into its interpretability and reliability for socially interactive applications. |
Explaining Mixtures of Sources in News Articles (2024.findings-emnlp)
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| Challenge: | a recent study shows that language models are essential for long-form article generation. |
| Approach: | They propose a generative process where a source-selection schema is first selected by a journalist, and then sources are chosen based on categories in that schema. |
| Outcome: | The proposed model can predict the most suitable schema given just the headline with reasonable accuracy. |
Identifying Informational Sources in News Articles (2023.emnlp-main)
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| Challenge: | Identifying sources of information in news articles is relevant to many tasks in NLP, including misinformation detection and argumentation. |
| Approach: | They propose a task to study compositionality of sources in news articles to understand how they are chosen to complement each other. |
| Outcome: | The proposed dataset can be used to train high-performing models for information detection and source attribution. |
Controlled Text Generation with Hidden Representation Transformations (2023.findings-acl)
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| Challenge: | Using a con-trolled language model, we gain attribute control by modifying the hidden representation of thebase model through learning transformations. |
| Approach: | They propose a con-trolled language generation framework that gains attribute control bymodifying the hidden representation of thebase model through learned transformations. |
| Outcome: | The proposed framework outperforms all thebaselines in detoxification, positivesentiment steering, and text simplification while minimizing the loss in linguistic qualities. |
Tracking the Newsworthiness of Public Documents (2024.acl-long)
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| Challenge: | a new method to model news coverage of local government is needed . we show that newsworthiness predictions can be useful for journalists seeking to keep abreast of local governments. |
| Approach: | They propose a method that explicitly models when and why stories get press attention . they use an annotated corpus of news articles to build models that predict if a policy item will get covered . |
| Outcome: | The proposed model outperforms retrieval-based methods with limited annotated data and language use between corpora. |
GRAVITY: A Framework for Personalized Text Generation via Profile-Grounded Synthetic Preferences (2026.eacl-long)
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| Challenge: | Personalization in LLMs often relies on costly human feedback or interaction logs, limiting scalability and neglecting deeper user attributes. |
| Approach: | They propose a framework for generating synthetic, profile-grounded preference data that captures users’ interests, values, beliefs, and personality traits. |
| Outcome: | The proposed framework improves on book descriptions for 400 Amazon users across multiple cultures, with user studies showing that outputs are preferred over 86% of the time. |
Can Language Model Moderators Improve the Health of Online Discourse? (2024.naacl-long)
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Hyundong Cho, Shuai Liu, Taiwei Shi, Darpan Jain, Basem Rizk, Yuyang Huang, Zixun Lu, Nuan Wen, Jonathan Gratch, Emilio Ferrara, Jonathan May
| Challenge: | Existing efforts to automate conversational moderation have focused on banning harmful comments or deleting them, but such efforts can inadvertently push users towards echo chambers that exacerbate polarization. |
| Approach: | They propose a framework to assess models’ moderation capabilities independently of human intervention and propose 'conversational moderation' they propose to use language models as conversational moderators to provide specific feedback on toxic behavior but struggle to influence users to increase their levels of respect and cooperation. |
| Outcome: | The proposed framework assesses models’ moderation capabilities independently of human intervention and shows that appropriately prompted models provide specific and fair feedback on toxic behavior but struggle to influence users to increase their levels of respect and cooperation. |