Rohit Avinash Mujumdar

arXiv2026

"I had nobody to turn to": Why LGBTQ+ People Choose (Not) to Use AI Chatbots for Personal Health and Relationship Advice

While there is an abundance of research into large language model (LLM) biases against LGBTQ+ communities, far less research has examined how and why LGBTQ+ people choose to make use of AI chatbots. In this work, we examine how LGBTQ+ people choose (not) to use AI chatbots across three domains in which LGBTQ+ users have unique needs: relationship support, mental health support, and gender or sexual health support. We surveyed 73 people (60 LGBTQ+; 13 non-LGBTQ+) on how they interact with AI chatbots and find that participants' access to external resources, privacy concerns, and perceptions of chatbot capabilities shape whether and how they choose to make use of AI chatbots. For instance, some LGBTQ+ participants felt that chatbots might actually be less biased against LGBTQ+ people than the people they know offline. We also find that LGBTQ+ participants report using AI chatbots less frequently than non-LGBTQ+ participants across 15 specific personal health and relationship advice scenarios, citing concerns about AI sycophancy and adverse cognitive side-effects. Recognizing AI chatbots as vital (albeit imperfect) resources for LGBTQ+ people, we recommend researchers consider systemic issues that drive users to AI chatbots and design technologies (AI or otherwise) that prioritize privacy, anonymity, and real human connection.

cs.HC↗

arXiv2026

MOSS: A Production Framework for Monitoring and Diagnosing Semantic Failures in Search and Recommendation Systems

Detecting semantic failures in production recommendation and search systems is hard: behavioral metrics lag by days, manual review does not scale, and most LLM evaluation frameworks are designed for offline, fixed datasets rather than continuous operational monitoring. We present Model Sanity Service (MOSS), a production framework that addresses this operational gap by embedding LLM-based relevance scoring into a recurring monitoring loop with targeted sampling, strategy-aware prompting, structured alerting, and diagnostic follow-up prompting that categorizes failures into actionable root causes. MOSS has been running in production at a large U.S. retailer for over six months across recommendation, search, and offer surfaces, and has surfaced two confirmed serving defects. For one of these, a backfill blending defect, MOSS quantified the impact of the proposed fix offline as a 76.4\% relative reduction in alert rate. On a 498-pair human-annotated recommendation benchmark, frontier LLM judges achieve higher F1 and AUC point estimates than non-LLM baselines (internal production embeddings and SBERT), with the largest gains on strategy-dependent tasks requiring intent-level relational reasoning. The same judge, score scale, and threshold transfer to a 466-pair search benchmark spanning 21 queries with only a surface-specific prompt adaptation, achieving strong F1 and AUC without retuning. These results show that a single LLM monitoring framework can provide useful, actionable quality signals across multiple product discovery surfaces in a large-scale e-commerce setting.

cs.IR↗