169 citations · 299 across the 17 of their papers we have counts for
6 papers · 1 filter
Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferences
Scott Sanner, Krisztian Balog, Filip Radlinski +2
Traditional recommender systems leverage users' item preference history to recommend novel content that users may like. However, modern dialog interfaces that allow users to expres…
Large Language Models for User Interest Journeys
Konstantina Christakopoulou, Alberto Lalama, Cj Adams +10
Large language models (LLMs) have shown impressive capabilities in natural language understanding and generation. Their potential for deeper user understanding and improved persona…
KNNs of Semantic Encodings for Rating Prediction
Léo Laugier, Raghuram Vadapalli, Thomas Bonald +1
This paper explores a novel application of textual semantic similarity to user-preference representation for rating prediction. The approach represents a user's preferences as a gr…
Simfluence: Modeling the Influence of Individual Training Examples by Simulating Training Runs
Kelvin Guu, Albert Webson, Ellie Pavlick +3
Training data attribution (TDA) methods offer to trace a model's prediction on any given example back to specific influential training examples. Existing approaches do so by assign…
Gradient-Based Automated Iterative Recovery for Parameter-Efficient Tuning
Maximilian Mozes, Tolga Bolukbasi, Ann Yuan +3
Pretrained large language models (LLMs) are able to solve a wide variety of tasks through transfer learning. Various explainability methods have been developed to investigate their…
Towards Agile Text Classifiers for Everyone
Maximilian Mozes, Jessica Hoffmann, Katrin Tomanek +5
Text-based safety classifiers are widely used for content moderation and increasingly to tune generative language model behavior - a topic of growing concern for the safety of digi…