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20222026
most citedAn Audit on the Perspectives and Challenges of Hallucinations in NLP

7 citations · 24 across the 25 of their papers we have counts for

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9 papers · 1 filter

cs.CL2026

Many Ways to Be Fake: Benchmarking Fake News Detection Under Strategy-Driven AI Generation

Xinyu Wang, Sai Koneru, Wenbo Zhang +3

Recent advances in large language models (LLMs) have enabled the large-scale generation of highly fluent and deceptive news-like content. While prior work has often treated fake ne…

cs.CL2026

Context Selection for Hypothesis and Statistical Evidence Extraction from Full-Text Scientific Articles

Sai Koneru, Jian Wu, Sarah Rajtmajer

Extracting hypotheses and their supporting statistical evidence from full-text scientific articles is central to the synthesis of empirical findings, but remains difficult due to d…

cs.CL2026

Evaluating Evidence Grounding Under User Pressure in Instruction-Tuned Language Models

Sai Koneru, Elphin Joe, Christine Kirchhoff +2

In contested domains, instruction-tuned language models must balance user-alignment pressures against faithfulness to the in-context evidence. To evaluate this tension, we introduc…

cs.CL2025★ 1 cited

A Tale of Two Identities: An Ethical Audit of Human and AI-Crafted Personas

Pranav Narayanan Venkit, Jiayi Li, Yingfan Zhou +2

As LLMs (large language models) are increasingly used to generate synthetic personas particularly in data-limited domains such as health, privacy, and HCI, it becomes necessary to…

cs.CL2025

Can Third-parties Read Our Emotions?

Jiayi Li, Yingfan Zhou, Pranav Narayanan Venkit +4

Natural Language Processing tasks that aim to infer an author's private states, e.g., emotions and opinions, from their written text, typically rely on datasets annotated by third-…

cs.CL2024

Have LLMs Reopened the Pandora's Box of AI-Generated Fake News?

Xinyu Wang, Wenbo Zhang, Sai Koneru +5

With the rise of AI-generated content spewed at scale from large language models (LLMs), genuine concerns about the spread of fake news have intensified. The perceived ability of L…

cs.CL2024★ 6 cited

Monolingual and Multilingual Misinformation Detection for Low-Resource Languages: A Comprehensive Survey

Xinyu Wang, Wenbo Zhang, Sarah Rajtmajer

In today's global digital landscape, misinformation transcends linguistic boundaries, posing a significant challenge for moderation systems. Most approaches to misinformation detec…

cs.CL2024

The Unappreciated Role of Intent in Algorithmic Moderation of Social Media Content

Xinyu Wang, Sai Koneru, Pranav Narayanan Venkit +2

As social media has become a predominant mode of communication globally, the rise of abusive content threatens to undermine civil discourse. Recognizing the critical nature of this…

cs.CL2024★ 7 cited

An Audit on the Perspectives and Challenges of Hallucinations in NLP

Pranav Narayanan Venkit, Tatiana Chakravorti, Vipul Gupta +5

We audit how hallucination in large language models (LLMs) is characterized in peer-reviewed literature, using a critical examination of 103 publications across NLP research. Throu…