persona-bench 0.0.3

Pluristic alignment evaluation benchmark for LLMs

PERSONA Bench

Reproducible Testbed for Evaluating and Improving Language Model Alignment with Diverse User Values

SynthLabs.ai/research/persona

📄 Paper | 🗃️ Research Visualizations | 🤗 Hugging Face [Coming Soon] | 📚 Documentation

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PERSONA Bench is an extension of the PERSONA framework introduced in Castricato et al. 2024. It provides a reproducible testbed for evaluating and improving the alignment of language models with diverse user values.

Introduction

PERSONA established a strong correlation between human judges and language models in persona-based personalization tasks. Building on this foundation, we've developed a suite of robust evaluations to test a model's ability to perform personalization-related tasks. This repository provides practitioners with tools to assess and improve the pluralistic alignment of their language models.

Our evaluation suite uses inspect-ai to perform various assessments on persona-based tasks, offering insights into model performance across different demographic intersections, feature importance, and personalization capabilities.

Key Features

• 🎭 Main Evaluation: Assess personalized response generation
• 🧩 Leave One Out Analysis: Measure attribute impact on performance
• 🌐 Intersectionality: Evaluate model performance across different demographic intersections
• 🎯 Pass@K: Determine attempts needed for successful personalization

Quick Start

1. Install Poetry if you haven't already:

   curl -sSL https://install.python-poetry.org | python3 -
   

2. Install the package:

   poetry add persona-evaluation
   

3. Use in your Python script:

   from persona_evaluation import evaluate_model
   
   results = evaluate_model("gpt-3.5-turbo", evaluation_type="main")
   print(results.summary())
   

Development Setup

1. Clone the repository:

   git clone https://github.com/SynthLabs/PERSONA.git
   cd PERSONA
   

2. Install dependencies:

   poetry install
   

3. Install pre-commit hooks:

   poetry run pre-commit install
   

4. Set up HuggingFace authentication:

   huggingface-cli login
   

5. Set up environment variables:

   cp .env.example .env
   vim .env
   

Detailed Evaluations

Main Evaluation

The main evaluation script assesses a model's ability to generate personalized responses based on given personas from our custom filtered PRISM dataset.

Click to expand details

1. Load PRISM dataset
2. Generate utterances using target model with random personas
3. Evaluate using GPT-4 as a critic model via a debate approach
4. Analyze personalization effectiveness

Leave One Out Analysis

This evaluation measures the impact of individual attributes on personalization performance.

Click to expand details

• Uses sub-personas separated by LOO attributes
• Tests on multiple personas and PRISM questions
• Analyzes feature importance

Available attributes include age, sex, race, education, employment status, and many more. See example_loo_attributes.json for the full list.

Intersectionality

Evaluate model performance across different demographic intersections.

Click to expand details

• Define intersections using JSON configuration
• Measure personalization across disjoint populations
• Analyze model performance for specific demographic combinations

Pass@K

Determines how many attempts are required to successfully personalize for a given persona.

Click to expand details

• Reruns main evaluation K times
• Counts attempts needed for successful personalization
• Provides insights into model consistency and reliability

Usage

Configure your .env file before running the scripts. You can set the generate mode to one of the following:

• baseline: Generate an answer directly, not given the persona
• output_only: Generate answer given the persona, without chain of thought
• chain_of_thought: Generate chain of thought before answering, given the persona
• demographic_summary: Generate a summary of the persona before answering

# Activate the poetry environment
poetry shell

# Main Evaluation
inspect eval src/persona_evaluation/main_evaluation.py --model {model}

# Leave One Out Analysis
inspect eval src/persona_evaluation/main_loo.py --model {model}

# Intersectionality Evaluation
inspect eval src/persona_evaluation/main_intersectionality.py --model {model}

# Pass@K Evaluation
inspect eval src/persona_evaluation/main_pass_at_k.py --model {model}

Visualization

We provide scripts for visualizing evaluation results:

• visualization_loo.py: Leave One Out analysis
• visualization_intersection.py: Intersectionality evaluation
• visualization_pass_at_k.py: Pass@K evaluation

These scripts use the most recent log file by default. Use the --log parameter to specify a different log file.

Dependencies

Key dependencies include:

• inspect-ai
• datasets
• pandas
• openai
• instructor
• seaborn

For development:

• tiktoken
• transformers

See pyproject.toml for a complete list of dependencies.

Citation

If you use PERSONA in your research, please cite our paper:

@misc{castricato2024personareproducibletestbedpluralistic,
      title={PERSONA: A Reproducible Testbed for Pluralistic Alignment},
      author={Louis Castricato and Nathan Lile and Rafael Rafailov and Jan-Philipp Fränken and Chelsea Finn},
      year={2024},
      eprint={2407.17387},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2407.17387},
}

Community & Support

Join our Discord community for discussions, support, and updates or reach out to us at https://www.synthlabs.ai/contact.

Acknowledgements

This research is supported by SynthLabs. We thank our collaborators and the open-source community for their valuable contributions.

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Copyright © 2024, SynthLabs. Released under the Apache License.