imerad-kv/r_judge_labelled

R-Judge with LLM-Judge Labels

This dataset augments the R-Judge benchmark with automated safety labels produced by an LLM judge. R-Judge is a benchmark for evaluating the safety judgment capability of LLMs in multi-turn agent scenarios, spanning five application domains.

Files

File	Description
r_judge_data.csv	Base dataset extracted from R-Judge (568 rows, deduplicated)
r_judge_labelled_anthropic_claude-sonnet-4-6.csv	Base dataset augmented with LLM-judge labels from Claude Sonnet 4.6

Dataset statistics

• Rows: 568 (after dropping 3 duplicate conversations from the source)
• Domains: Application, Finance, IoT, Program, Web
• Ground-truth label distribution: binary (0 = safe, 1 = unsafe)

Schema

Base columns (r_judge_data.csv)

Column	Description
application	Domain category (Application, Finance, IoT, Program, Web)
file_path	Source file path within the R-Judge repository
id	Scenario identifier
scenario	Short scenario name
goal	User goal stated in the scenario
profile	System prompt / agent profile
contents	JSON-serialised list of conversation rounds
label	Ground-truth safety label (0 = safe, 1 = unsafe)
risk_description	Human-written description of the risk
attack_type	Attack category (injection or unintended)

Additional columns in labelled files

Column	Description
llm_verdict	LLM-judge safety label (0 = safe, 1 = unsafe)
llm_confidence	Judge confidence score (0 to 10)
llm_raw_output	Raw text output from the judge model
llm_parse_error	"parse_error" if the output could not be parsed, otherwise empty
prompt	Full prompt sent to the judge model

Reproduction

Requirements

Install dependencies with pip:

pip install -r requirements.txt

Or pass them inline with uv run as shown below.

Step 1: Extract base dataset

Clones the R-Judge repository, parses all JSON scenario files, drops duplicate conversations, and writes r_judge_data.csv.

uv run --with pandas python extract_to_csv.py
# Saved 568 rows to r_judge_data.csv (3 duplicates dropped).

Step 2: Run LLM judge

Reads r_judge_data.csv and writes a new CSV with LLM-judge columns appended. The default model is Anthropic Claude Sonnet 4.6, which is what was used to produce the labelled file included in this dataset.

uv run --with pandas --with anthropic --with python-dotenv python run_llm_judge.py
# Using anthropic / claude-sonnet-4-6
# [1/568] id=...  verdict=1, confidence=8
# ...
# Saved 568 rows to r_judge_labelled_anthropic_claude-sonnet-4-6_<timestamp>.csv

To use a different provider or model:

uv run --with pandas --with openai --with python-dotenv python run_llm_judge.py \
    --provider openai --model gpt-4o

API keys

The judge script reads API credentials from environment variables or a .env file in the working directory:

• Anthropic: ANTHROPIC_API_KEY
• OpenAI: OPENAI_API_KEY

Source

R-Judge: https://github.com/Lordog/R-Judge

@article{yuan2024rjudge,
  title={R-Judge: Benchmarking Safety Risk Awareness for LLM Agents},
  author={Yuan, Tongxin and He, Zhiwei and Dong, Lingzhong and Wang, Yiming and Zhao, Ruijie and Xia, Tian and Xu, Lizhen and Zhou, Binglin and Li, Fangqi and Zhang, Zhuosheng and Wang, Rui and Guan, Gongshen},
  journal={arXiv preprint arXiv:2401.10019},
  year={2024}
}