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Found 13 papers, 5 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.
Automatic Histograms: Leveraging Language Models for Text Dataset Exploration
Making sense of unstructured text datasets is perennially difficult, yet increasingly relevant with Large Language Models.
LLM Comparator: Visual Analytics for Side-by-Side Evaluation of Large Language Models
Automatic side-by-side evaluation has emerged as a promising approach to evaluating the quality of responses from large language models (LLMs).
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.
Analyzing a Caching Model
Machine Learning has been successfully applied in systems applications such as memory prefetching and caching, where learned models have been shown to outperform heuristics.
Best of both worlds: local and global explanations with human-understandable concepts
While there are many methods focused on either one, few frameworks can provide both local and global explanations in a consistent manner.
The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models
We present the Language Interpretability Tool (LIT), an open-source platform for visualization and understanding of NLP models.
The Bach Doodle: Approachable music composition with machine learning at scale
To make music composition more approachable, we designed the first AI-powered Google Doodle, the Bach Doodle, where users can create their own melody and have it harmonized by a machine learning model Coconet (Huang et al., 2017) in the style of Bach.
The What-If Tool: Interactive Probing of Machine Learning Models
A key challenge in developing and deploying Machine Learning (ML) systems is understanding their performance across a wide range of inputs.
Towards Automatic Concept-based Explanations
Interpretability has become an important topic of research as more machine learning (ML) models are deployed and widely used to make important decisions.
ClinicalVis: Supporting Clinical Task-Focused Design Evaluation
Making decisions about what clinical tasks to prepare for is multi-factored, and especially challenging in intensive care environments where resources must be balanced with patient needs.
Human-Computer Interaction
Scalable and accurate deep learning for electronic health records
Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality.
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)
The interpretation of deep learning models is a challenge due to their size, complexity, and often opaque internal state.
