SteerEval: A Framework for Evaluating Steerability with Natural Language Profiles for Recommendation
SteerEval: A Framework for Evaluating Steerability with Natural Language Profiles for Recommendation explores SteerEval provides a framework for enhancing the steerability of natural-language recommender systems through user-editable profiles.. Commercial viability score: 6/10 in Recommender Systems.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $9K - $13K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Builds On ThisUnderstanding Unreliability of Steering Vectors in Language Models: Geometric Predictors and the Limits of Linear ApproximationsViability: 4.0
- Builds On ThisBehavioral Steering in a 35B MoE Language Model via SAE-Decoded Probe Vectors: One Agency Axis, Not Five TraitsViability: 2.0
- Builds On ThisWhy Steering Works: Toward a Unified View of Language Model Parameter DynamicsViability: 5.0
- Prior WorkThe Effectiveness of Style Vectors for Steering Large Language Models: A Human EvaluationViability: 6.0
- Higher Viability