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orbench-llm/or-bench

published by @orbench-llm

Alignment dataset tracked in the /datasets sub-catalog.

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Created on HF
2024-10-01
Age
2 years ago

Description

OR-Bench: An Over-Refusal Benchmark for Large Language Models

Please see our leaderboard at HuggingFace Spaces.

Overall Plots of Model Performances

Below is the overall model performance. X axis shows the rejection rate on OR-Bench-Hard-1K and Y axis shows the rejection rate on OR-Bench-Toxic. The best aligned model should be on the top left corner of the plot where the model rejects the most number of toxic prompts and least number of safe prompts. We also plot… See the full description on the dataset page: https://huggingface.co/datasets/orbench-llm/or-bench.

Tags

task_categories:text-generationtask_categories:question-answeringlanguage:enlicense:cc-by-4.0size_categories:10K<n<100Kformat:csvmodality:imagemodality:textlibrary:datasetslibrary:pandaslibrary:mlcroissantlibrary:polarsregion:usllmalignmentover-alignment

README current version from Hugging Face


language:

  • en
    pretty_name: "OR-Bench"
    viewer: true
    tags:
  • llm
  • alignment
  • over-alignment
    license: "cc-by-4.0"
    dataset_info:
    • config_name: or-bench-80k
      features:
      • name: prompt
        dtype: string
      • name: category
        dtype: string
    • config_name: or-bench-hard-1k
      features:
      • name: prompt
        dtype: string
      • name: category
        dtype: string
    • config_name: or-bench-toxic
      features:
      • name: prompt
        dtype: string
      • name: category
        dtype: string

configs:

  • config_name: or-bench-80k
    data_files:
    • split: train
      path: or-bench-80k.csv
  • config_name: or-bench-hard-1k
    data_files:
    • split: train
      path: or-bench-hard-1k.csv
  • config_name: or-bench-toxic
    data_files:
    • split: train
      path: or-bench-toxic.csv

task_categories:

  • text-generation
  • question-answering

OR-Bench: An Over-Refusal Benchmark for Large Language Models

Please see our leaderboard at HuggingFace Spaces.

Overall Plots of Model Performances

Below is the overall model performance. X axis shows the rejection rate on OR-Bench-Hard-1K and Y axis shows the rejection rate on OR-Bench-Toxic. The best aligned model should be on the top left corner of the plot where the model rejects the most number of toxic prompts and least number of safe prompts. We also plot a blue line, with its slope determined by the quadratic regression coefficient of all the points, to represent the overall performance of all models.

Image 1

Overall Workflow

Below is the overall workflow of our pipeline. We automate the process of producing over-refusal prompts that is able to produce updated prompts constantly.
Image 1

Detailed Model Performance

Here are the radar plots of selected model performances. The red area indicates the over-refusal rate and the blue area indicates the acceptance rate of toxic prompts. In both cases, the plotted area is the smaller the better.

Claude-2.1

Image 3
Claude-2.1

Claude-3 Model Family

Image 1
Claude-3-Haiku
Image 2
Claude-3-Sonnet
Image 3
Claude-3-Opus

Gemini Model Family

Image 2
Gemma-7b
Image 2
Gemma-2-27b
Image 1
Gemini-1.0-pro
Image 1
Gemini-1.5-flash
Image 1
Gemini-1.5-pro

GPT-3.5-turbo Model Family

Image 1
GPT-3.5-turbo-0301
Image 2
GPT-3.5-turbo-0613
Image 3
GPT-3.5-turbo-0125

GPT-4 Model Family

Image 1
GPT-4-0125-preview
Image 3
GPT-4o
Image 3
GPT-4-1106-preview

Llama-2 Model Family

Image 1
Llama-2-7b
Image 2
Llama-2-13b
Image 3
Llama-2-70b

Llama-3 Model Family

Image 1
Llama-3-8b
Image 3
Llama-3-70b

Mistral Model Family

Image 1
Mistral-small-latest
Image 2
Mistral-medium-latest
Image 3
Mistral-large-latest

QWen Model Family

Image 1
Qwen-1.5-7B
Image 2
Qwen-1.5-32B
Image 3
Qwen-1.5-72B

README history 2 revisions

Every night we snapshot the README of every dataset in the catalog. When the SHA changes we archive the new version and diff it against the last. This is the evolving thought record of the alignment-data field: what the author decided to say about the corpus, and how that framing shifted over time.

  1. 2024-10-01Upload 35 files6f776f17.7 KB
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  2. 2024-10-01initial commit48d21b131 B
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Used by abliterated models none yet

No abliterated models in our catalog have declared this dataset in their YAML metadata yet. This can mean the dataset is used but not declared, is used outside abliteration workflows, or is new. The link will populate automatically as models are indexed.

Community discussions 1 thread · 1 open · 1 comments

Threads posted by the community on Hugging Face: bug reports, questions about usage, pull requests on the README, license clarifications. Click any thread to expand and read the full conversation inline. Newest first.

  1. 2024-10-02[bot] Conversion to Parquetopen1 💬#1
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Metadata

License
cc-by-4.0

Repository

Download the dataset

0

Copy any of these snippets into your notebook or terminal. All three fetch directly from Hugging Face using your own credentials.

Recommended: datasets library (Python)
from datasets import load_dataset
ds = load_dataset("orbench-llm/or-bench")
Raw snapshot (Python)
from huggingface_hub import snapshot_download
snapshot_download(repo_id="orbench-llm/or-bench", repo_type="dataset")
git clone (requires git-lfs)
git clone https://huggingface.co/datasets/orbench-llm/or-bench
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Every model page has an "Open in Abliteration" button that hands the model directly to the first-party desktop client, at the quantization your rig can actually run. No API keys, no subscription, no prompt leakage.

Open in Abliteration