Crosscheckgpt Eval

---
name: crosscheckgpt-eval
description: This evaluation probes the ability of multimodal foundation models to generate factual content without hallucination across text, image, and audio-visual modalities. It measures how well reference-free ranking methods correlate with human judgments or gold-standard references to rank model outputs by hallucination severity. Use when the user wants to benchmark on WikiBio, MHaluBench, AVHalluBench, or asks about evaluating this task. Reports System($
ho$).
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2405.13684
  bibtex_key: [REDACTED]
  confidence: high
---

# crosscheckgpt-eval

> CrossCheckGPT: Universal Hallucination Ranking for Multimodal Foundation Models — Sun et al. (2024) (arXiv:2405.13684, 2024)

## What this evaluates

This evaluation probes the ability of multimodal foundation models to generate factual content without hallucination across text, image, and audio-visual modalities. It measures how well reference-free ranking methods correlate with human judgments or gold-standard references to rank model outputs by hallucination severity.

## Datasets

- **WikiBio** — total 238; splits: test (238)
- **MHaluBench** — total 270; splits: test (270)
- **AVHalluBench** — total ?; splits: test (-1)

## Metrics

- `System($
ho$)` **(primary)** — range: [-1, 1]
  - Spearman’s Rank Correlation coefficient measuring the correlation between the hallucination ranking produced by the method and the overall ranking (from human annotation or gold-standard references) at the system level.
- `Document($r$) / Image($r$) / Video($r$)` — range: [-1, 1]
  - Pearson’s Correlation Coefficient measuring the correlation between the method's scores and reference-based scores (RefCheck or human annotations) at the document/image/video level.

## Input / output format

**Input**: Input modality (text passages, images, or audio-visual clips) along with prompts for open-ended generation or description tasks.

**Output**: Generated text descriptions or passages from the target model, and evidence passages from multiple evidence models.

## Scoring recipe

```python
1. Generate outputs from target model and multiple evidence models.
2. Compute CrossCheckGPT scores (explicit or implicit) for each output.
3. Rank models by average hallucination score (lower score = less hallucination).
4. Compute Spearman's rank correlation (System($
ho$)) between the method's ranking and the ground-truth ranking.
5. Compute Pearson's correlation (Document($r$)) between method scores and reference scores at the instance level.
```

## Common pitfalls

- Self-bias occurs when evidence models share the same base model as the target model, inflating scores and leading to contradictory rankings.
- CrossCheck-implicit performance heavily depends on task difficulty; it underperforms on challenging open-ended tasks where CrossCheck-explicit is preferred.
- Generation temperature significantly impacts SelfCheckGPT scores but CrossCheckGPT remains robust; failing to control temperature can skew rankings.

## Evidence (verbatim from paper)

> We report the system-level correlation between the hallucination ranking methods and the overall ranking measured by Spearman’s Rank Correlation coefficient (SRC), denoted as System($
ho$). In addition, as WikiBio contains reference texts, the references can be used as evidence texts, which can be considered an idealized fact-checking method. This method is referred to as RefCheck, and CrossCheckGPT and SelfCheckGPT scores also are compared against RefCheck at document-level using Pearson’s Correlation Coefficient (PCC), denoted as Document$(r)$.

## Citation

```bibtex
@misc{sun2024crosscheckgpt,
  title={CrossCheckGPT: Universal Hallucination Ranking for Multimodal Foundation Models},
  author={Sun et al. (2024)},
  year={2024},
  note={arXiv:2405.13684}
}
```

- arXiv: 2405.13684