Papers by Shreya Havaldar
Building Knowledge-Guided Lexica to Model Cultural Variation (2024.naacl-long)
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| Challenge: | Cultural variation exists between nations, but also within regions . Historically, it has been difficult to computationally model cultural variation due to a lack of training data and scalability constraints. |
| Approach: | They propose a method to measure cultural variation using a knowledge-guided lexical model using geolocated tweets. |
| Outcome: | The proposed method could help us better understand the way people communicate and build more culturally-aware NLP systems. |
Comparing Styles across Languages (2023.emnlp-main)
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| Challenge: | Communication practices vary across cultures. Inherent differences in how people think and behave influence cultural norms. |
| Approach: | They propose a framework to extract stylistic differences from multilingual language models (LMs) they use a multilingual lexica to consolidate feature importances into comparable lexical categories . |
| Outcome: | The proposed framework generates comprehensive style lexica in any language and consolidates feature importances from LMs into comparable lexical categories. |
Towards Style Alignment in Cross-Cultural Translation (2025.acl-long)
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| Challenge: | Successful communication relies on the speaker’s intended style aligning with the listener’s interpreted style. |
| Approach: | They propose a method that leverages learned stylistic concepts to encourage LLM translation to appropriately convey cultural communication norms and align style. |
| Outcome: | The proposed method aims to encourage translations to convey cultural communication norms and align style. |
Social Norms in Cinema: A Cross-Cultural Analysis of Shame, Pride and Prejudice (2025.naacl-long)
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| Challenge: | We examine *how* and *why* shame and pride are expressed across cultures using a blend of psychology-informed language analysis combined with large language models. |
| Approach: | They introduce a cross-cultural dataset of over 10k shame/pride-related expressions with underlying social expectations from 5.4K Bollywood and Hollywood movies. |
| Outcome: | The results show that women are more sanctioned across cultures and for violating similar social expectations. |
Entailed Between the Lines: Incorporating Implication into NLI (2025.acl-long)
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Shreya Havaldar, Hamidreza Alvari, John Palowitch, Mohammad Javad Hosseini, Senaka Buthpitiya, Alex Fabrikant
| Challenge: | True Emotions, social cues, insults, and a myriad of other messages are conveyed implicitly, often even more so than explicitly. |
| Approach: | They propose a dataset to help LLMs understand implied entailment . |
| Outcome: | The proposed dataset enables LLMs to understand implied entailment and can generalize this understanding across datasets and domains. |
Probabilistic Soundness Guarantees in LLM Reasoning Chains (2025.emnlp-main)
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| Challenge: | Existing methods for detecting propagated errors in reasoning chains are inadequate . author et al. (2017) show that initial errors propagate and undermine reliability of final conclusion . |
| Approach: | They propose a framework that evaluates each reasoning step based solely on previously-verified premises and provides certified statistical guarantees of its soundness. |
| Outcome: | ARES achieves state-of-the-art performance across four benchmarks and demonstrates superior robustness on very long synthetic reasoning chains. |
Adaptively profiling models with task elicitation (2025.emnlp-main)
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| Challenge: | Language model evaluations fail to characterize consequential failure modes, forcing experts to inspect outputs and build new benchmarks. |
| Approach: | They propose a method that automatically builds new evaluations to profile model behavior. |
| Outcome: | The proposed method finds that language models fail in hundreds of tasks . it also finds that o3-mini is prone to hallucination when fabrications are repeated . |