Routing Without Embeddings: Fast And Interpretable Routing With Regular Expressions
cs.LG
Submitted: 2026-09-28
Updated: 2026-09-28
Code: https://github.com/tatsu-lab/stanford_alpaca
Terminology
Sources
- Crafting Interpretable Embeddings by Asking LLMs Questions
- A is for Absorption: Studying Feature Splitting and Absorption in Sparse Autoencoders
- Qwen-Scope: Turning Sparse Features into Development Tools for Large Language Models
- BEST-Route: Adaptive LLM Routing with Test-Time Optimal Compute
- Bayesian Concept Bottleneck Models with LLM Priors
- GraphRouter: A Graph-based Router for LLM Selections
- Scaling and evaluating sparse autoencoders
- Gemma 2: Improving Open Language Models at a Practical Size
- The Llama 3 Herd of Models
- Llama Scope: Extracting Millions of Features from Llama-3.1-8B with Sparse Autoencoders
- Universal Model Routing for Efficient LLM Inference
- OpenAssistant Conversations -- Democratizing Large Language Model Alignment
- Sparse Autoencoders Do Not Find Canonical Units of Analysis
- Gemma Scope: Open Sparse Autoencoders Everywhere All At Once on Gemma 2
- RouterArena: An Open Platform for Comprehensive Comparison of LLM Routers
- The Routing Plateau: Understanding the Accuracy Limits of LLM Routers
- Interpretable-by-Design Text Understanding with Iteratively Generated Concept Bottleneck
- RouteLLM: Learning to Route LLMs with Preference Data
- Automatically Interpreting Millions of Features in Large Language Models
- Qwen2.5 Technical Report
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