arXiv subjects

S M Mehedi Zaman

Publications and source records attributed to S M Mehedi Zaman.

13 recordsLinked to original sources

arXiv2026

Vulnerabilities in Personalization: Assessing Health Privacy Risks in ChatGPT Logs and Memory

As conversational LLMs become deeply embedded in daily life, users frequently disclose sensitive personal health information during routine interactions. We present a large-scale computational audit analyzing 179,057 conversations across India, Nigeria, Brazil, and Pakistan (N = 1,057) to evaluate personal health disclosures and background memory synthesis in ChatGPT. We find that 21.31% of audited conversations contain personal health data, with 3.62% posing high-to-extreme privacy risks involving stigmatized conditions, direct identifiers, and precise locations. When evaluating the memory entries of ChatGPT, we uncover a stark disconnect between corporate framing and system behavior: over 95% of profile entries are implicitly extracted without explicit user prompts or consent. Furthermore, background memory synthesis selectively condenses temporary, symptom-level disclosures into permanent diagnostic traits, stripping contextual integrity and amplifying re-identification risks. We conclude with sociotechnical design guidelines to restore user agency and consent-driven boundaries in stateful AI systems.

cs.HC↗

arXiv2026

How Students (Really) Use ChatGPT: Uncovering Experiences Among Undergraduate Students

We examine how undergraduate students integrate ChatGPT into everyday self-directed learning, analyzing 10,536 naturalistic messages donated by 36 students over a year. A sequential mixed-methods pipeline pairs iterative qualitative coding with zero-shot language-model annotation validated against human labels (kappa = 0.75-0.91). It yields a five-category taxonomy: Information Seeking, Content Generation, Language Use, Student-ChatGPT Interaction, and ChatGPT Response Behavior. Time-lagged linear regression and Cox proportional-hazards models link these categories to sustained engagement. Three findings stand out. First, structured tasks (theory application, code writing, job-application content, multiple-choice questions) predict continued use; ChatGPT becomes incorporated into academic rhythms when gratifications are reliably fulfilled. Second, system-issued "apologies" are the strongest positive predictor of increased engagement, outweighing every task-completion predictor. We name this mechanism "repair gratification": the reward of a breakdown acknowledged and repaired rather than a task simply completed. Third, interactional strain--prompt revision, frustration, follow-up clarification--predicts disengagement. When managing the system falls on the user without system accountability, students abandon the tool. We interpret these results through Self-Directed Learning, Uses and Gratifications Theory, and Expectancy Violations Theory, mapping predictors onto positive/negative violations and confirmations. We close with design recommendations for graduated repair patterns, mode-aware interaction, and verification affordances, and outline a participatory AI-literacy agenda for higher education.

cs.HC↗

arXiv2026

From Retrieval to Synthesis: Repair Literacy and the Domestication of Generative AI

How do students develop AI literacy through everyday practice rather than formal instruction? While normative AI literacy frameworks proliferate, empirical understanding of how students actually learn to work with generative AI remains limited. This study analyzes 10,536 ChatGPT messages from 36 undergraduates over one academic year, revealing five use genres -- academic workhorse, emotional companion, metacognitive partner, repair and negotiation, and trust calibration -- that constitute distinct configurations of student-AI learning. Drawing on domestication theory and emerging frameworks for AI literacy, we demonstrate that functional AI competence emerges through ongoing relational negotiation rather than one-time adoption. Students develop sophisticated genre portfolios, strategically matching interaction patterns to learning needs while exercising critical judgment about AI limitations. Notably, repair work during AI breakdowns produces substantial learning about AI capabilities, developing what we term "repair literacy" -- a crucial but underexplored dimension of AI competence. Our findings offer educators empirically grounded insights into how students actually learn to work with generative AI, with implications for AI literacy pedagogy, responsible AI integration, and the design of AI-enabled learning environments that support student agency.

cs.HC↗

arXiv2026

Inferential Privacy Leakage in Anonymized Conversational AI Logs

Hundreds of millions of users now hold detailed, multi-turn conversations with ChatGPT and similar LLM assistants. We measure two privacy-relevant features of these conversations on a corpus of complete ChatGPT histories donated by over 1,000 users in four Global South countries (Brazil, India, Nigeria, Pakistan). First, on explicit disclosure: 34.5% of user messages contain personal information across a twenty-category taxonomy, with the median user first revealing identifying content within the first 14% of their conversation history. Second, on inference beyond explicit disclosure: we restrict to a cohort whose conversations contain no messages flagged by an LLM-based filter for explicit demographic self-identification (a separate NER pass marks PII for the disclosure audit but does not drive cohort exclusion). On this filtered cohort, an off the shelf large language model still recovers each user's age, gender, and country at weighted F1 of 0.84, 0.90, and 0.88, respectively, with the median user identified from the first 5% of their conversation history. Reading the model's natural-language reasoning traces, we identify four recurring stereotype patterns that drive both successful inference and an asymmetric error distribution concentrating on women in technical fields, older users with contemporary skills, and Global South tech professionals. We also compare ChatGPT against the same users' Google Search and YouTube histories as inference surfaces, and find it competitive with these older substrates that have driven behavioral advertising for two decades. Message-level PII removal is insufficient on its own as a privacy intervention for conversational AI data.

cs.CY↗

arXiv2026

Demographic Divides in Political Content Exposure on Facebook

Despite Facebook's central role in American civic life, a clear, evidence-based understanding of users' long-term information environments has remained elusive, hindering assessments of the platform's societal impact. This study addresses that gap by analyzing a unique decade-long dataset, constructed by collecting the full list of public pages and groups followed by over 1,100 American users. This approach allows us to examine the potential information exposure of these users by analyzing hundreds of millions of posts from 2012 to 2023. We find that political content constitutes a modest 18% of a user's potential information diet, which is predominantly composed of lifestyle and entertainment topics. This aggregate view, however, masks a deeply stratified reality: we uncover significant and persistent disparities in the volume and ideological leaning of political content across age, gender, and racial lines. Furthermore, we quantify the porous boundaries between content categories, showing how political discourse frequently permeates non-political spaces. Leveraging the dataset's longitudinal nature, we also assess the impact of major platform interventions. We find that Meta's 2018 "Meaningful Social Interactions" update dramatically increased the share of political content by contracting the visibility of non-political posts. By providing a granular, decade-long map of potential information exposure, our study offers one of the first representative and longitudinal picture drawn from platform-independent data. Our findings underscore the critical need for researchers to measure exposure, not merely engagement, and to account for the significant volume of political content that circulates in non-political spaces.

cs.SI↗

arXiv2026

Privacy Concerns and ChatGPT: Exploring Online Discourse through the Lens of Information Practice on Reddit

As millions of people use ChatGPT for tasks such as education, writing assistance, and health advice, concerns have grown about how personal prompts and data are stored and used. This study explores how Reddit users collectively negotiate and respond to these privacy concerns. Posts were collected from three major subreddits -- r/Chatgpt, r/privacy, and r/OpenAI -- between November 2022 and May 2025. An iterative keyword search followed by manual screening resulted in a final dataset of 426 posts and 1,900 comments. Using information practice as the theoretical lens, we conducted a qualitative thematic analysis to identify collective practices of risk negotiation, validated with BERTopic topic modeling to ensure thematic saturation. Findings revealed risk signaling, norm-setting, and resignation as dominant discourses, and collective troubleshooting and advocacy for privacy-preserving alternatives as key adaptive practices. Reddit functions as a site of collective sense-making where users surface risks, establish informal norms, and share strategies for mitigating privacy threats, offering insights for AI design and privacy literacy initiatives.

cs.CY↗

arXiv2025

Disagreement is Disappearing on U.S. Cable Debate Shows

Prime-time cable news programs are a highly influential part of the American media landscape, with top-rated opinion shows attracting millions of politically attentive viewers each night. In an era of intense political polarization, a critical question is whether these widely-watched "debate" shows foster genuine discussion or have devolved into partisan echo chambers that deepen societal divides. While these programs claim to air competing viewpoints, no large-scale evidence exists to quantify how often hosts and guests actually disagree. Measuring these exchanges is a significant challenge, as live broadcasts contain overlapping speakers, sarcasm, and billions of words of text. To address this gap, we construct the first speaker-resolved map of agreement and disagreement across U.S. cable opinion programming. Our study assembles over 21,000 episodes from 24 flagship shows on Fox News, MSNBC, and CNN from 2010-2024, segmenting them into host-guest turns and labeling 2.13 million turn-pairs using a high-fidelity large-language-model classifier. We present three findings: (1) the proportion of disagreement/debate on prime time shows a consistent downward trend, dropping by roughly one-third between 2017 and 2024; (2) on-air challenge is partisan and asymmetric--conservatives seldom face push-back on Fox, liberals seldom on MSNBC, with CNN declining toward the midpoint; and (3) polarizing issues such as abortion, gun rights, and immigration attract the least disagreement. The work contributes a public corpus, an open-source stance pipeline, and the first longitudinal evidence that televised "debate" is retreating from genuine discussion. By transforming into platforms for partisan affirmation, these shows erode the cross-cutting cleavages essential for a pluralistic society, thereby intensifying affective polarization.

cs.SI↗

arXiv2024

"Sorry, Come Again?" Prompting -- Enhancing Comprehension and Diminishing Hallucination with [PAUSE]-injected Optimal Paraphrasing

Hallucination has emerged as the most vulnerable aspect of contemporary Large Language Models (LLMs). In this paper, we introduce the Sorry, Come Again (SCA) prompting, aimed to avoid LLM hallucinations by enhancing comprehension through: (i) optimal paraphrasing and (ii) injecting [PAUSE] tokens to delay LLM generation. First, we provide an in-depth analysis of linguistic nuances: formality, readability, and concreteness of prompts for 21 LLMs, and elucidate how these nuances contribute to hallucinated generation. Prompts with lower readability, formality, or concreteness pose comprehension challenges for LLMs, similar to those faced by humans. In such scenarios, an LLM tends to speculate and generate content based on its imagination (associative memory) to fill these information gaps. Although these speculations may occasionally align with factual information, their accuracy is not assured, often resulting in hallucination. Recent studies reveal that an LLM often neglects the middle sections of extended prompts, a phenomenon termed as lost in the middle. While a specific paraphrase may suit one LLM, the same paraphrased version may elicit a different response from another LLM. Therefore, we propose an optimal paraphrasing technique to identify the most comprehensible paraphrase of a given prompt, evaluated using Integrated Gradient (and its variations) to guarantee that the LLM accurately processes all words. While reading lengthy sentences, humans often pause at various points to better comprehend the meaning read thus far. We have fine-tuned an LLM with injected [PAUSE] tokens, allowing the LLM to pause while reading lengthier prompts. This has brought several key contributions: (i) determining the optimal position to inject [PAUSE], (ii) determining the number of [PAUSE] tokens to be inserted, and (iii) introducing reverse proxy tuning to fine-tune the LLM for [PAUSE] insertion.

cs.CL↗

arXiv2024

The What, Why, and How of Context Length Extension Techniques in Large Language Models -- A Detailed Survey

The advent of Large Language Models (LLMs) represents a notable breakthrough in Natural Language Processing (NLP), contributing to substantial progress in both text comprehension and generation. However, amidst these advancements, it is noteworthy that LLMs often face a limitation in terms of context length extrapolation. Understanding and extending the context length for LLMs is crucial in enhancing their performance across various NLP applications. In this survey paper, we delve into the multifaceted aspects of exploring why it is essential, and the potential transformations that superior techniques could bring to NLP applications. We study the inherent challenges associated with extending context length and present an organized overview of the existing strategies employed by researchers. Additionally, we discuss the intricacies of evaluating context extension techniques and highlight the open challenges that researchers face in this domain. Furthermore, we explore whether there is a consensus within the research community regarding evaluation standards and identify areas where further agreement is needed. This comprehensive survey aims to serve as a valuable resource for researchers, guiding them through the nuances of context length extension techniques and fostering discussions on future advancements in this evolving field.

cs.CL↗

arXiv2024

A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models

As Large Language Models (LLMs) continue to advance in their ability to write human-like text, a key challenge remains around their tendency to hallucinate generating content that appears factual but is ungrounded. This issue of hallucination is arguably the biggest hindrance to safely deploying these powerful LLMs into real-world production systems that impact people's lives. The journey toward widespread adoption of LLMs in practical settings heavily relies on addressing and mitigating hallucinations. Unlike traditional AI systems focused on limited tasks, LLMs have been exposed to vast amounts of online text data during training. While this allows them to display impressive language fluency, it also means they are capable of extrapolating information from the biases in training data, misinterpreting ambiguous prompts, or modifying the information to align superficially with the input. This becomes hugely alarming when we rely on language generation capabilities for sensitive applications, such as summarizing medical records, financial analysis reports, etc. This paper presents a comprehensive survey of over 32 techniques developed to mitigate hallucination in LLMs. Notable among these are Retrieval Augmented Generation (Lewis et al, 2021), Knowledge Retrieval (Varshney et al,2023), CoNLI (Lei et al, 2023), and CoVe (Dhuliawala et al, 2023). Furthermore, we introduce a detailed taxonomy categorizing these methods based on various parameters, such as dataset utilization, common tasks, feedback mechanisms, and retriever types. This classification helps distinguish the diverse approaches specifically designed to tackle hallucination issues in LLMs. Additionally, we analyze the challenges and limitations inherent in these techniques, providing a solid foundation for future research in addressing hallucinations and related phenomena within the realm of LLMs.

cs.CL↗

arXiv2023

Counter Turing Test CT^2: AI-Generated Text Detection is Not as Easy as You May Think -- Introducing AI Detectability Index

With the rise of prolific ChatGPT, the risk and consequences of AI-generated text has increased alarmingly. To address the inevitable question of ownership attribution for AI-generated artifacts, the US Copyright Office released a statement stating that 'If a work's traditional elements of authorship were produced by a machine, the work lacks human authorship and the Office will not register it'. Furthermore, both the US and the EU governments have recently drafted their initial proposals regarding the regulatory framework for AI. Given this cynosural spotlight on generative AI, AI-generated text detection (AGTD) has emerged as a topic that has already received immediate attention in research, with some initial methods having been proposed, soon followed by emergence of techniques to bypass detection. This paper introduces the Counter Turing Test (CT^2), a benchmark consisting of techniques aiming to offer a comprehensive evaluation of the robustness of existing AGTD techniques. Our empirical findings unequivocally highlight the fragility of the proposed AGTD methods under scrutiny. Amidst the extensive deliberations on policy-making for regulating AI development, it is of utmost importance to assess the detectability of content generated by LLMs. Thus, to establish a quantifiable spectrum facilitating the evaluation and ranking of LLMs according to their detectability levels, we propose the AI Detectability Index (ADI). We conduct a thorough examination of 15 contemporary LLMs, empirically demonstrating that larger LLMs tend to have a higher ADI, indicating they are less detectable compared to smaller LLMs. We firmly believe that ADI holds significant value as a tool for the wider NLP community, with the potential to serve as a rubric in AI-related policy-making.

cs.CL↗

arXiv2023

Exploring the Relationship between LLM Hallucinations and Prompt Linguistic Nuances: Readability, Formality, and Concreteness

As Large Language Models (LLMs) have advanced, they have brought forth new challenges, with one of the prominent issues being LLM hallucination. While various mitigation techniques are emerging to address hallucination, it is equally crucial to delve into its underlying causes. Consequently, in this preliminary exploratory investigation, we examine how linguistic factors in prompts, specifically readability, formality, and concreteness, influence the occurrence of hallucinations. Our experimental results suggest that prompts characterized by greater formality and concreteness tend to result in reduced hallucination. However, the outcomes pertaining to readability are somewhat inconclusive, showing a mixed pattern.

cs.AI↗

arXiv2023

Atmospheric Influence on the Path Loss at High Frequencies for Deployment of 5G Cellular Communication Networks

Over the past few decades, the development of cellular communication technology has spanned several generations in order to add sophisticated features in the updated versions. Moreover, different high-frequency bands are considered for advanced cellular generations. The presence of updated generations like 4G and 5G is driven by the rising demand for a greater data rate and a better experience for end users. However, because 5G-NR operates at a high frequency and has significant propagation, atmospheric fluctuations like temperature, humidity, and rain rate might result in poorer signal reception, and higher path loss effects unlike the prior generation, which employed frequencies below 6 GHz. This paper makes an attempt to provide a comparative analysis about the influence of different relative atmospheric conditions on 5G cellular communication for various operating frequencies in any urban microcell (UMi) environment maintaining the real outdoor propagation conditions. In addition, the simulation dataset based on environmental factors has been validated by the prediction of path loss using multiple regression techniques. Consequently, this study also aims to address the performance analysis of regression techniques for stable estimations of path loss at high frequencies for different atmospheric conditions for 5G mobile generations due to various possible radio link quality issues and fluctuations in different seasons in South Asia. Furthermore, in comparison to contemporary studies, the Machine Learning models have outperformed in predicting the path loss for the four seasons in South Asian regions.

eess.SP↗