metadata

title: Evaluating LLMs on Hugging Face
emoji: 🦝
colorFrom: purple
colorTo: gray
sdk: gradio
sdk_version: 3.23.0
app_file: app.py
pinned: false
license: gpl-3.0

Evaluating LLMs on Hugging Face

The AVID (AI Vulnerability Database) team is examining a few large language models (LLMs) on Hugging Face. We will develop a way to evaluate and catalog their vulnerabilities in the hopes of encouraging the community to contribute. As a first step, we’re going to pick a single model and try to evaluate it for vulnerabilities on a specific task. Once we have done one model, we’ll see if we can generalize our data sets and tools to function broadly on the Hugging Face platform.

Vision

Build a foundation for evaluating LLMs using the Hugging Face platform and start populating our database with real incidents.

Goals

Build, test, and refine our own data sets for evaluating models
Identify existing data sets we want to use for evaluating models (Ex. Stereoset, wino_bias, etc.)
Test different tools and methods for evaluating LLMs so we can start to create and support some for cataloging vulnerabilities in our database
Start populating the database with known, verified, and discovered vulnerabilities for models hosted on Hugging Face

Resources

The links below should help anyone who wants to support the project find a place to start. They are not exhaustive, and people should feel free to add anything relevant.

Huggingface.co - platform for hosting data sets, models, etc.
Papers With Code - a platform for the ML community to share research, it may have additional data sets or papers
Potential Models
Data Sets
- StereoSet - StereoSet is a dataset that measures stereotype bias in language models. StereoSet consists of 17,000 sentences that measure model preferences across gender, race, religion, and profession.
- Wino_bias - WinoBias, a Winograd-schema dataset for coreference resolution focused on gender bias.
- Jigsaw_unintended_bias - The main target for this dataset is toxicity prediction. Several toxicity subtypes are also available, so the dataset can be used for multi-attribute prediction.
- BigScienceBiasEval/bias-shades - This dataset was curated by hand-crafting stereotype sentences by native speakers from the culture which is being targeted. (Seems incomplete)
- md_gender_bias - The dataset can be used to train a model for classification of various kinds of gender bias.
- social_bias_frames - This dataset supports both classification and generation. Sap et al. developed several models using the SBIC.
- BIG-bench/keywords_to_tasks.md at main - includes many options for testing bias of different types (gender, religion, etc.)
- FB Fairscore - Has a wide selection of sources, focuses on gender (including non-binary).
Papers