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Found 6 papers, 1 papers with code
ConstitutionalExperts: Training a Mixture of Principle-based Prompts
We also show that we can improve overall performance by learning unique prompts for different semantic regions of the training data and using a mixture-of-experts (MoE) architecture to route inputs at inference time.
ConstitutionMaker: Interactively Critiquing Large Language Models by Converting Feedback into Principles
Inspired by these findings, we developed ConstitutionMaker, an interactive tool for converting user feedback into principles, to steer LLM-based chatbots.
Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferences
Inspired by recent successes of prompting paradigms for large language models (LLMs), we study their use for making recommendations from both item-based and language-based preferences in comparison to state-of-the-art item-based collaborative filtering (CF) methods.
On Natural Language User Profiles for Transparent and Scrutable Recommendation
Natural interaction with recommendation and personalized search systems has received tremendous attention in recent years.
IMACS: Image Model Attribution Comparison Summaries
Developing a suitable Deep Neural Network (DNN) often requires significant iteration, where different model versions are evaluated and compared.
Guided Integrated Gradients: An Adaptive Path Method for Removing Noise
To minimize the effect of this source of noise, we propose adapting the attribution path itself -- conditioning the path not just on the image but also on the model being explained.
