4 papers
From Time and Place to Preference: LLM-Driven Geo-Temporal Context in Recommendations
Yejin Kim, Shaghayegh Agah, Mayur Nankani +5
Most recommender systems treat timestamps as numeric or cyclical values, overlooking real-world context such as holidays, events, and seasonal patterns. We propose a scalable frame…
Predicting Movie Hits Before They Happen with LLMs
Shaghayegh Agah, Yejin Kim, Neeraj Sharma +4
Addressing the cold-start issue in content recommendation remains a critical ongoing challenge. In this work, we focus on tackling the cold-start problem for movies on a large ente…
Causal Reasoning in Large Language Models: A Knowledge Graph Approach
Yejin Kim, Eojin Kang, Juae Kim +1
Large language models (LLMs) typically improve performance by either retrieving semantically similar information, or enhancing reasoning abilities through structured prompts like c…
Prompts have evil twins
Rimon Melamed, Lucas H. McCabe, Tanay Wakhare +3
We discover that many natural-language prompts can be replaced by corresponding prompts that are unintelligible to humans but that provably elicit similar behavior in language mode…