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)