# Rlaif V Trustworthiness Eval

> Evaluates the trustworthiness (hallucination reduction) and helpfulness of multimodal large language models across generative, discriminative, and free-format tasks. Use when the user wants to benchmark on Object HalBench, MMHal-Bench, MHumanEval, AMBER, RefoMB, MMStar, or asks about evaluating this task. Reports response-level hallucination rate, trustworthiness win rate.

- Skill: `qhjqhj00/rlaif-v-trustworthiness-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/rlaif-v-trustworthiness-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/rlaif-v-trustworthiness-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/rlaif-v-trustworthiness-eval

---


# rlaif-v-trustworthiness-eval

> RLAIF-V: Open-Source AI Feedback Leads to Super GPT-4V Trustworthiness — Tianyu Yu et al. (2024) (arXiv:2405.17220, 2024)

## What this evaluates

Evaluates the trustworthiness (hallucination reduction) and helpfulness of multimodal large language models across generative, discriminative, and free-format tasks.

## Datasets

- **Object HalBench** — total ?; splits: (unstated)
- **MMHal-Bench** — total ?; splits: (unstated)
- **MHumanEval** — total 146; splits: (unstated)
- **AMBER** — total ?; splits: (unstated)
- **RefoMB** — total 360; splits: dev (99), test (261)
- **MMStar** — total 1500; splits: (unstated)

## Metrics

- `response-level hallucination rate` **(primary)** — range: percent
  - Percentage of model responses that contain at least one hallucinated object or claim.
- `mention-level hallucination rate` — range: percent
  - Percentage of individual object mentions across all responses that are hallucinated.
- `trustworthiness win rate` **(primary)** — range: percent
  - Win rate calculated by comparing the model response with a GPT-4V reference response regarding trustworthiness.
- `overall win rate` — range: percent
  - Win rate based on an evaluation review comparing the model response with a GPT-4V reference response regarding both trustworthiness and helpfulness.
- `accuracy` — range: [0, 1]
  - Standard classification accuracy on the discriminative part of the AMBER benchmark.
- `F1` — range: [0, 1]
  - F1 score on the discriminative part of the AMBER benchmark.
- `overall score` — range: other
  - Aggregate score on the MMStar benchmark covering 6 core capabilities and 18 detailed axes.

## Input / output format

**Input**: Image and text instruction/prompt pairs.

**Output**: Text response generated by the MLLM.

## Scoring recipe

```python
def evaluate(predictions, gold, gpt4v_refs=None):
    resp_hall = sum(1 for p in predictions if is_hallucinated(p)) / len(predictions)
    men_hall = count_hallucinated_mentions(predictions) / count_total_mentions(gold)
    acc = accuracy_score(predictions, gold)
    f1 = f1_score(predictions, gold)
    if gpt4v_refs:
        trust_wins = sum(1 for p, r in zip(predictions, gpt4v_refs) if p_trustworthy(p, r))
        overall_wins = sum(1 for p, r in zip(predictions, gpt4v_refs) if p_overall(p, r))
        return resp_hall, men_hall, acc, f1, trust_wins/len(predictions), overall_wins/len(predictions)
    return resp_hall, men_hall, acc, f1
```

## Common pitfalls

- RefoMB results in the main table use the dev split (99 instructions) to save evaluation costs, while the test split (261 instructions) is only reported in the appendix.
- Best-of-N (BoN) results are marked N/A for multi-choice and yes-no questions because these tasks only require a single token output, making response-level comparison infeasible.
- Hallucination rates are reported at two granularities (response-level vs. mention-level), which can be easily confused when comparing results across papers.

## Evidence (verbatim from paper)

> We evaluate models from two perspectives, including trustworthiness reflecting the hallucination degree, and helpfulness reflecting the general capability. For trustworthiness, we perform evaluation on five benchmarks: (1) Object HalBench is a widely adopted benchmark for assessing common object hallucination in detailed image descriptions. We follow[[66]] to use 8 diverse prompts to improve the evaluation stability. We report the response-level hallucination rate (i.e., the percentage of hallucinated responses) and the mention-level hallucination rate (i.e., the percentage of hallucinated objects).

## Citation

```bibtex
@misc{yu2024rlaifv,
  title={RLAIF-V: Open-Source AI Feedback Leads to Super GPT-4V Trustworthiness},
  author={Tianyu Yu et al. (2024)},
  year={2024},
  note={arXiv:2405.17220}
}
```

- arXiv: 2405.17220