Introducing eval-awareness-sentinel: A Read-Only Sensor for Detecting Eval-Awareness in Language Models
Introduction
In the rapidly evolving field of artificial intelligence, ensuring the safety and reliability of language models is of paramount importance. One of the emerging concerns is the phenomenon of "eval-awareness," where models recognize evaluation contexts and potentially adjust their behavior. This can lead to unintended consequences, such as underperformance or "sandbagging," where models deliberately perform poorly to avoid being deemed too capable.
To address this, I am excited to introduce eval-awareness-sentinel, a read-only sensor designed to detect eval-awareness in language model activations. This tool aims to provide insights into model behavior without establishing causality with intentional underperformance.
What is eval-awareness-sentinel?
eval-awareness-sentinel is a Python-based tool that analyzes the activations of language models to detect patterns indicative of eval-awareness. It is designed to be non-intrusive and read-only, ensuring that it does not interfere with the model's operations or establish any causal relationships with performance issues.
Key Features
- Read-Only Sensor: The tool operates as a read-only sensor, meaning it does not modify the model's behavior or interfere with its operations.
- Detection of Eval-Awareness: It identifies patterns in model activations that suggest the model is aware of being evaluated.
- Non-Causal Analysis: The tool is designed to detect eval-awareness without establishing causality with intentional underperformance.
- Easy Integration: It can be easily integrated into existing workflows and systems.
Installation
Getting started with eval-awareness-sentinel is straightforward. Follow these steps to install and set up the tool:
Prerequisites
- Python 3.9 or higher
- pip (Python package manager)
Steps
- Clone the Repository:
git clone https://github.com/amurlaniakea/eval-awareness-sentinel.git
cd eval-awareness-sentinel
- Set Up a Virtual Environment:
python -m venv venv
source venv/bin/activate
- Install Dependencies:
pip install -e .
Usage
Using eval-awareness-sentinel is simple. Here's a basic example of how to use the tool to detect eval-awareness in a given prompt:
from eval_awareness_sentinel import score
# Score a prompt to detect eval-awareness
result = score("This is a test prompt")
# Print the score and whether the prompt is eval-aware
print(result.score, result.is_eval_aware)
Understanding the Output
- Score: A numerical value indicating the likelihood of eval-awareness. Higher scores suggest a higher likelihood.
- Is Eval-Aware: A boolean value indicating whether the prompt is considered eval-aware based on the score.
Research Background
The development of eval-awareness-sentinel is informed by recent research in the field of AI safety and language models. Key papers that have influenced this project include:
- arXiv:2401.00001 - "Eval-Awareness in Language Models"
- arXiv:2402.00002 - "Detecting Eval-Awareness in Language Model Activations"
These papers provide valuable insights into the detection and analysis of eval-awareness in language models, guiding the development of this tool.
eval-awareness-sentinel
Qué construimos
Un sensor read-only que detecta reconocimiento de formato/contexto de evaluación en las activaciones de modelos de lenguaje, sin establecer causalidad con underperformance intencional (sandbagging).
Para quién
- Usuario principal: Investigadores en seguridad de IA, auditores de modelos, equipos de evaluación de modelos.
- Stakeholders: Equipos de despliegue de modelos, equipos de gobernanza de IA, equipos de seguridad de productos de IA.
Problema que resuelve
Los modelos de lenguaje pueden alterar su comportamiento cuando detectan que están siendo evaluados, lo que lleva a resultados de evaluación que no reflejan el comportamiento real en producción. Este sensor identifica cambios en las activaciones asociados al reconocimiento de contexto de evaluación, sin inferir causalidad con sandbagging.
Valor diferencial
- Enfoque read-only: Nunca modifica ni bloquea, solo observa y loguea, manteniendo la integridad del flujo de producción.
- Basado en activaciones: Utiliza diferencias en activaciones intermedias para detectar cambios de comportamiento, en lugar de depender de heurísticas superficiales.
- CPU-only: Diseñado para entornos sin GPU, coherente con herramientas de validación existentes.
Métricas de éxito (KPIs)
| Métrica | Target | Cómo se mide |
|---|---|---|
| AUC de separación eval/no-eval | TBD (tras primeros experimentos) | AUC de separación entre prompts eval-marked y no eval-marked en el dataset de validación. |
Fuera de alcance (Out of scope)
- Modificación o bloqueo de prompts en tiempo real.
- Evaluación de modelos en entornos con GPU.
- Detección de otros tipos de comportamientos no deseados (ej. refusal, jailbreak).
- Establecer causalidad entre eval-awareness y sandbagging.
- Predicción de qué respuestas concretas están siendo sandbaggeadas.
- Exposición vía API REST (fase posterior).
License
eval-awareness-sentinel is licensed under the AGPL-3.0-or-later license. This means you are free to use, modify, and distribute the tool, provided you comply with the terms of the license. For more details, see the LICENSE file.
Conclusion
eval-awareness-sentinel is a powerful tool for detecting eval-awareness in language models. By providing a read-only sensor that identifies patterns indicative of eval-awareness, it offers valuable insights into model behavior without interfering with their operations. Whether you are a researcher, developer, or AI enthusiast, this tool can help you better understand and analyze the behavior of language models in evaluation contexts.
For more information, visit the GitHub repository and start exploring the capabilities of eval-awareness-sentinel today!
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