Papers by Sarah Rajtmajer
An Audit on the Perspectives and Challenges of Hallucinations in NLP (2024.emnlp-main)
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Pranav Narayanan Venkit, Tatiana Chakravorti, Vipul Gupta, Heidi Biggs, Mukund Srinath, Koustava Goswami, Sarah Rajtmajer, Shomir Wilson
| Challenge: | 103 peer-reviewed publications on hallucination in large language models (LLMs) are characterized by a lack of agreement with the term ‘hallucination’ in the field of NLP. |
| Approach: | They examine 103 peer-reviewed publications on hallucination in large language models (LLMs) and conduct a survey with 171 practitioners from the field of NLP and AI to capture varying perspectives on halllucination. |
| Outcome: | The findings highlight the need for explicit definitions and frameworks outlining hallucination within NLP and highlight potential challenges. |
Have LLMs Reopened the Pandora’s Box of AI-Generated Fake News? (2025.naacl-long)
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Xinyu Wang, Wenbo Zhang, Sai Koneru, Hangzhi Guo, Bonam Mingole, S. Shyam Sundar, Sarah Rajtmajer, Amulya Yadav
| Challenge: | Large language models (LLMs) are increasingly being used by fake news creators to generate deceptive and persuasive content at scale. |
| Approach: | They propose to use large language models to generate fake news at scale and to assess the ability of human annotators and AI models to detect it. |
| Outcome: | The results show that LLMs are 68% more effective at detecting real news than humans, compared to humans and AI models for fake news detection. |
Private Seeds, Public LLMs: Realistic and Privacy-Preserving Synthetic Data Generation (2026.findings-acl)
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| Challenge: | Large language models (LLMs) are a powerful tool for creating synthetic replicas of private text. |
| Approach: | They propose a method for creating privacy-preserving synthetic data using private seeds and a formal differential privacy mechanism. |
| Outcome: | The proposed method achieves high fidelity to private data while providing strong privacy protection. |
Can Large Language Models Discern Evidence for Scientific Hypotheses? Case Studies in the Social Sciences (2024.lrec-main)
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| Challenge: | scholarly databases fail to aggregate, compare, contrast, and contextualize existing studies in service to a targeted research question. |
| Approach: | They propose to use large language models to discern evidence in support or refute of specific hypotheses based on abstracts. |
| Outcome: | The proposed method outperforms state-of-the-art methods and highlights opportunities for future research. |
Can Third Parties Read Our Emotions? (2025.acl-long)
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Jiayi Li, Yingfan Zhou, Pranav Narayanan Venkit, Halima Binte Islam, Sneha Arya, Shomir Wilson, Sarah Rajtmajer
| Challenge: | Existing approaches to infer author’s private states from written text have relied heavily on datasets annotated by third-party annotators. |
| Approach: | They propose a framework for evaluating the limitations of third-party annotations and call for refined annotation practices to accurately represent and model authors’ private states. |
| Outcome: | The proposed methods outperform human annotators on emotion recognition tasks. |
A Semantics-based Approach to Disclosure Classification in User-Generated Online Content (2020.findings-emnlp)
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| Challenge: | Existing algorithms for self-disclosure identification and classification are challenging due to the relative anonymity of social networking sites and lack of non-verbal cues to signal thoughts or feelings. |
| Approach: | They propose an approach to detect emotional and informational self-disclosure in natural language by using frame semantics to identify lexical units and their semantic roles. |
| Outcome: | The proposed method improves on reddit data and provides insights into the drivers of disclosure behaviors. |