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…
Estimating Semantic Alphabet Size for LLM Uncertainty Quantification
Lucas H. McCabe, Rimon Melamed, Thomas Hartvigsen +1
Many black-box techniques for quantifying the uncertainty of large language models (LLMs) rely on repeated LLM sampling, which can be computationally expensive. Therefore, practica…
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…
Demystifying optimized prompts in language models
Rimon Melamed, Lucas H. McCabe, H. Howie Huang
Modern language models (LMs) are not robust to out-of-distribution inputs. Machine generated (``optimized'') prompts can be used to modulate LM outputs and induce specific behavior…