Kritika Venkatachalam

arXiv2026

Communication styles and reader preferences of LLM- and human-authored COVID-19 information explanations: a case study

With the wide adoption of large language models (LLMs) in information assistance, it is essential to examine their alignment with human communication styles and values. We situate this study within health fact-checking, where effective communication is critical for correcting misconceptions and building trust. Although recent studies have explored LLMs for fact-checking and health communication, differences between LLM and human communication styles and associated reader perceptions remain under-explored. We compiled a dataset of 1,498 health misinformation claims and explanations from authoritative fact-checking organizations and generated LLM responses to inaccurate health information. Drawing on health communication theories, we evaluated communication styles across three dimensions: information linguistic features, sender persuasive strategies, and receiver value alignments. We further assessed reader perceptions through a blinded evaluation with 99 participants. LLM-generated articles scored significantly lower in persuasive strategies, certainty expressions, and alignment with social values and moral foundations. However, participants strongly preferred LLM content, with over 60% of responses favoring LLM articles for clarity, completeness, and persuasiveness. This preference was associated with structured presentation, clarity, and neutral tone, which may convey completeness and professionalism despite reduced nuance or rigor. These findings highlight both the potential and limitations of LLMs in health communication and fact-checking, suggesting that reader preference may not necessarily correspond to established measures of communication quality.

cs.HC↗

arXiv2021

SERC: Syntactic and Semantic Sequence based Event Relation Classification

Temporal and causal relations play an important role in determining the dependencies between events. Classifying the temporal and causal relations between events has many applications, such as generating event timelines, event summarization, textual entailment and question answering. Temporal and causal relations are closely related and influence each other. So we propose a joint model that incorporates both temporal and causal features to perform causal relation classification. We use the syntactic structure of the text for identifying temporal and causal relations between two events from the text. We extract parts-of-speech tag sequence, dependency tag sequence and word sequence from the text. We propose an LSTM based model for temporal and causal relation classification that captures the interrelations between the three encoded features. Evaluation of our model on four popular datasets yields promising results for temporal and causal relation classification.

cs.CL↗