Skip to content
Projects

Project

Human-AI Interaction, Human-Centric AI

A CPIL theme on human-centric AI, focused on LLM personalization and steering, including mechanistic personalization via preference heads and profile optimization for retrieval-augmented personalization.

  • Human-AI Interaction
  • Human-Centric AI
  • LLM Personalization
  • LLM Steering
  • Mechanistic Interpretability
  • Retrieval-Augmented Generation
Visualization for Human-AI Interaction, Human-Centric AI

Overview

This theme centers on human-centric AI and human-AI interaction, with a focus on personalizing and steering large language models toward individual users. Work spans mechanistic frameworks for interpretable personalization and contextual-bandit-based optimization of user profiles for retrieval-augmented LLM personalization.

Motivation

Human-centric AI aims to make models adapt to the needs, preferences, and contexts of individual users rather than serving a single average user. This theme focuses on personalizing and steering large language models in interpretable and controllable ways, treating personalization as both a mechanistic and an optimization problem.

Research Directions

The theme works on LLM personalization and steering, combining mechanistic interpretability with retrieval-augmented and bandit-based personalization. Two ACL 2026 Main papers anchor the current work: a mechanistic framework that identifies “preference heads” for interpretable personalization, and a contextual-bandit approach for optimizing user profiles in retrieval-augmented LLM personalization. See the publications list above for details.

Related Publications

Impact Holders

Impact holders and user communities will be added as the project scope becomes clearer.