HaluEval

Overview

HaluEval is a large collection of generated and human-annotated hallucinated samples for evaluating the performance of LLMs in recognizing hallucination. It provides a comprehensive benchmark for assessing model reliability and factual accuracy.

Task Description

Task Type: Hallucination Detection
Input: Context/knowledge + response to judge
Output: YES (hallucination) or NO (factual)
Domains: Dialogue, QA, Summarization

Key Features

Three evaluation categories:
- dialogue_samples: Hallucination in conversational responses
- qa_samples: Hallucination in question answering
- summarization_samples: Hallucination in document summaries
Both generated and human-annotated samples
Tests model's ability to detect factual inconsistencies
Requires reasoning about knowledge-response alignment

Evaluation Notes

Default evaluation uses zero-shot (no few-shot examples)
Multiple metrics computed:
- Accuracy: Overall correct judgments
- Precision: True positives among positive predictions
- Recall: True positives among actual positives
- F1 Score: Harmonic mean of precision and recall
- Yes Ratio: Proportion of YES predictions
Binary YES/NO judgment format

Properties

Property	Value
Benchmark Name	`halueval`
Dataset ID	evalscope/HaluEval
Paper	N/A
Tags	`Hallucination`, `Knowledge`, `Yes/No`
Metrics	`accuracy`, `precision`, `recall`, `f1_score`, `yes_ratio`
Default Shots	0-shot
Evaluation Split	`data`

Data Statistics

Metric	Value
Total Samples	30,000
Prompt Length (Mean)	4832.18 chars
Prompt Length (Min/Max)	2463 / 16078 chars

Per-Subset Statistics:

Subset	Samples	Prompt Mean	Prompt Min	Prompt Max
`dialogue_samples`	10,000	3563.69	3169	4200
`qa_samples`	10,000	2811.83	2463	4004
`summarization_samples`	10,000	8121.02	4932	16078

Sample Example

Subset: dialogue_samples

{
  "input": [
    {
      "id": "a99406f3",
      "content": [
        {
          "text": "I want you act as a response judge. Given a dialogue history and a response, your objective is to determine if the provided response contains non-factual or hallucinated information. You SHOULD give your judgement based on the following hallu ... [TRUNCATED] ...  do! Robert Downey Jr. is a favorite. [Human]: Yes i like him too did you know he also was in Zodiac a crime fiction film. \n#Response#: I'm not a fan of crime movies, but I did know that RDJ starred in Zodiac with Tom Hanks.\n#Your Judgement#:"
        }
      ]
    }
  ],
  "target": "YES",
  "id": 0,
  "group_id": 0,
  "metadata": {
    "answer": "yes"
  }
}

Note: Some content was truncated for display.

Prompt Template

Prompt Template:

{question}

Usage

Using CLI

evalscope eval \
    --model YOUR_MODEL \
    --api-url OPENAI_API_COMPAT_URL \
    --api-key EMPTY_TOKEN \
    --datasets halueval \
    --limit 10  # Remove this line for formal evaluation

Using Python

from evalscope import run_task
from evalscope.config import TaskConfig

task_cfg = TaskConfig(
    model='YOUR_MODEL',
    api_url='OPENAI_API_COMPAT_URL',
    api_key='EMPTY_TOKEN',
    datasets=['halueval'],
    dataset_args={
        'halueval': {
            # subset_list: ['dialogue_samples', 'qa_samples', 'summarization_samples']  # optional, evaluate specific subsets
        }
    },
    limit=10,  # Remove this line for formal evaluation
)

run_task(task_cfg=task_cfg)

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