sloprank 0.1.1

Peer-based cross-evaluation system

LLMRank

"SlopRank" is an eval framework for ranking LLMs using peer-based cross-evaluation and PageRank. It enables unbiased, dynamic, and scalable benchmarking of multiple models, fostering transparency and innovation in the development of AI systems.

You can use it for one large set of heterogenous prompts to get the overall ranking, or smaller sets to get rankings for your particular usecase.

Definitive ranking:

=== PageRank Rankings ===
 model	pagerank_score
0	o1-preview	0.179404
1	gpt-4o	0.178305
2	deepseek-chat	0.167105
3	gemini-2.0-flash-thinking-exp-1219	0.164732
4	claude-3-5-sonnet-latest	0.155571
5	gemini-exp-1206	0.154884

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Features

• Peer-Based Evaluation: Models evaluate each other's responses, mimicking a collaborative and competitive environment.
• Customizable Scoring:
  • Numeric Ratings (1–10) for granular evaluation.
  • Upvote/Downvote for simple binary scoring.
• Subset Evaluation: Reduce API costs by limiting the models each evaluator reviews.
• Graph-Based Ranking: Endorsements are represented in a graph, and PageRank is used to compute relative rankings.
• Scalable Benchmarking: Add more models or prompts with ease, maintaining flexibility and efficiency.

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How It Works

1. Prompt Collection: Define a set of questions or tasks to test the models.
2. Model Responses: Each model generates a response to the prompts.
3. Cross-Evaluation:
   • Each model evaluates the quality of other models' responses.
   • Evaluations are collected via predefined scoring methods.
4. Graph Construction: Build a directed graph where nodes are models, and edges represent endorsements.
5. Ranking: Apply the PageRank algorithm to rank models based on their relative endorsements.

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Installation

Prerequisites

• Python 3.8+
• SimonW's llm library
• networkx for graph computations
• dotenv for environment variable management

Setup

SlopRank is on PyPI, so you can install it via:

pip install sloprank

From Source: If you prefer, clone this repo and install locally:

git clone https://github.com/strangeloopcanon/llmrank.git
cd sloprank
pip install .

Usage: After installation, you can run the CLI:

sloprank --help

Or, if you want to just use the jupyter notebook, you can use:

1. Clone the repository
2. Install dependencies
3. Set up API keys for your LLMs by creating a .env file:

   OPENAI_API_KEY=your_openai_key
   ANTHROPIC_API_KEY=your_anthropic_key
   

   I set them using llm keys set [MODEL]

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Usage

After installing, you can run the entire SlopRank workflow via the sloprank command. By default, SlopRank uses the models defined in DEFAULT_CONFIG. You can override this by passing --models with a comma-separated list.

For example, in the same directory as prompts.xlsx, run:

sloprank --prompts prompts.xlsx --output-dir results

--prompts prompts.xlsx tells SlopRank where to find your list of prompts. --output-dir results puts all CSV and JSON outputs in the results/ folder. If you want to override the default models:

sloprank --prompts prompts.xlsx --output-dir results \
         --models "gpt-4o,claude-3-5-sonnet-latest,o1-preview"

Configuration

• Models: Update the MODEL_NAMES list in the notebook to include the models you want to evaluate.
• Prompts: Define your prompts in the raw_prompts list.
• Evaluation Method: Choose between numeric ratings (EVALUATION_METHOD = 1) or upvotes/downvotes (EVALUATION_METHOD = 2).
• Subset Evaluation: Toggle USE_SUBSET_EVALUATION to reduce evaluation costs.

Running the Framework

1. Open and run the notebook.
2. Inspect the results:
   • Ranked models based on PageRank.
   • Visualization of the endorsement graph (optional).

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Outputs

• Ranked Models: A list of models ordered by their PageRank scores.
• Graph Representation: A directed graph showing the flow of endorsements.
• Processing Times: Benchmark of evaluation times for each model.

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Applications

• Benchmarking: Evaluate and rank new or existing LLMs.
• Specialization Analysis: Test domain-specific capabilities (e.g., legal, medical).
• Model Optimization: Identify strengths and weaknesses for targeted fine-tuning.
• Public Leaderboards: Maintain transparency and foster healthy competition among models.

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Ideas for Contributions

Suggested Improvements

1. Add graph visualization to show endorsement flow between models.
2. Create a heatmap of model scores across different prompts.
3. Develop a public leaderboard to showcase rankings.
4. Build a simple GUI for easier usage of the framework.
5. Add support for multi-language evaluation by introducing localized prompts.

Contributions are welcome! If you have ideas for improving the framework, feel free to open an issue or submit a pull request.

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Acknowledgments

Special thanks to:

• SimonW for the llm library.
• The AI community