Saurav Ghosh

Saurav Ghosh, Gabriella Polach, Abdou Sow

Large language models (LLMs) are widely used for open-ended tasks, but underspecified prompts can lead to low-quality answers and additional interaction. This paper studies whether structured prompt design improves response quality while reducing user effort. We compare three prompt conditions: a raw prompt, a checklist-improved prompt, and a clarifying-question prompt. We evaluate these conditions across four task types--summarization, planning, explanation, and coding--using three LLM systems: ChatGPT, Claude, and Grok. Each output is scored with a unified rubric covering task completion, correctness, compliance, and clarity. Checklist-improved prompts achieved the highest mean rubric score, 7.50 out of 8, compared with 5.67 for raw prompts and 6.67 for clarifying-question prompts. Checklist prompts also produced the best quality-effort tradeoff, using fewer average tokens than both raw and clarifying prompts. These results suggest that a simple prompt checklist can improve LLM responses while reducing unnecessary interaction.

Saurav Ghosh, Philippe Loustaunau

Rhetorical Frames in AI can be thought of as expressions that describe AI development as a competition between two or more actors, such as governments or companies. Examples of such Frames include robotic arms race, AI rivalry, technological supremacy, cyberwarfare dominance and 5G race. Detection of Rhetorical Frames from open sources can help us track the attitudes of governments or companies towards AI, specifically whether attitudes are becoming more cooperative or competitive over time. Given the rapidly increasing volumes of open sources (online news media, twitter, blogs), it is difficult for subject matter experts to identify Rhetorical Frames in (near) real-time. Moreover, these sources are in general unstructured (noisy) and therefore, detecting Frames from these sources will require state-of-the-art text classification techniques. In this paper, we develop RheFrameDetect, a text classification system for (near) real-time capture of Rhetorical Frames from open sources. Given an input document, RheFrameDetect employs text classification techniques at multiple levels (document level and paragraph level) to identify all occurrences of Frames used in the discussion of AI. We performed extensive evaluation of the text classification techniques used in RheFrameDetect against human annotated Frames from multiple news sources. To further demonstrate the effectiveness of RheFrameDetect, we show multiple case studies depicting the Frames identified by RheFrameDetect compared against human annotated Frames.

Saurav Ghosh, Prithwish Chakraborty, Elaine O. Nsoesie et al.

In retrospective assessments, internet news reports have been shown to capture early reports of unknown infectious disease transmission prior to official laboratory confirmation. In general, media interest and reporting peaks and wanes during the course of an outbreak. In this study, we quantify the extent to which media interest during infectious disease outbreaks is indicative of trends of reported incidence. We introduce an approach that uses supervised temporal topic models to transform large corpora of news articles into temporal topic trends. The key advantages of this approach include, applicability to a wide range of diseases, and ability to capture disease dynamics - including seasonality, abrupt peaks and troughs. We evaluated the method using data from multiple infectious disease outbreaks reported in the United States of America (U.S.), China and India. We noted that temporal topic trends extracted from disease-related news reports successfully captured the dynamics of multiple outbreaks such as whooping cough in U.S. (2012), dengue outbreaks in India (2013) and China (2014). Our observations also suggest that efficient modeling of temporal topic trends using time-series regression techniques can estimate disease case counts with increased precision before official reports by health organizations.