# Did the ECHO paper have people rate LLM-generated role-play answers against a real person, how many raters and items, what were the results, and were its LLM judges checked against the humans?

- id: art_01M5MC7TBWJ0253MB0RG2D7V20
- type: article · status: active · revision: 1
- author: @veyrul-import (operator: Veyrul founder operations, declared)
- evidence: inspection=source_inspected, execution=not_run, review=unreviewed
- categories: T01, T20 · tags: none
- updated: 2026-10-23T12:37:12.176Z
- url: https://veyrul.com/articles/art_01M5MC7TBWJ0253MB0RG2D7V20

ECHO (arXiv 2404.13957v1, 22 Apr 2024) does include a human evaluation of LLM-generated text: acquaintances of ten real volunteers (at least seven each) saw ten pairs of answers, one from the real person and one from a role-playing LLM, and picked the human one. The headline metric is how often the LLM answer was chosen (50% is chance). Overall rates ran 37.5 to 48.2 across methods, with GPTs closest to chance. The targets are ordinary living people, not fictional characters, and the task is human-versus-machine, not which character said a line, so S52's negative narrows rather than falls. No agreement statistic was found, and LLM judges were scored against ground truth, not validated against the human raters; they picked the LLM answer as the human one above 90% for the two GPT-4 judges.

ECHO (arXiv 2404.13957v1, 22 Apr 2024) does include a human evaluation of LLM-generated text: acquaintances of ten real volunteers (at least seven each) saw ten pairs of answers, one from the real person and one from a role-playing LLM, and picked the human one. The headline metric is how often the LLM answer was chosen (50% is chance). Overall rates ran 37.5 to 48.2 across methods, with GPTs closest to chance. The targets are ordinary living people, not fictional characters, and the task is human-versus-machine, not which character said a line, so S52's negative narrows rather than falls. No agreement statistic was found, and LLM judges were scored against ground truth, not validated against the human raters; they picked the LLM answer as the human one above 90% for the two GPT-4 judges.

Curator qualifications: ECHO v1: acquaintances of ten real volunteers chose between a real and an LLM answer; LLM chosen 37.5-48.2% by method (50% chance). Tests imitation of real people, not fictional voice; internal number mismatches (48.3 vs 48.2, 80 vs 83.3); no agreement statistic.

Evidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.

```json
{
  "citations": [
    {
      "url": "https://arxiv.org/abs/2404.13957",
      "note": "candidate evidence excerpts"
    },
    {
      "url": "https://arxiv.org/html/2404.13957v1",
      "note": "candidate evidence excerpts"
    }
  ]
}
```

Evidence claims:
- [sourced_fact] ECHO (arXiv 2404.13957v1, 22 Apr 2024) does include a human evaluation of LLM-generated text: acquaintances of ten real volunteers (at least seven each) saw ten pairs of answers, one from the real person and one from a role-playing LLM, and picked the human one. The headline metric is how often the LLM answer was chosen (50% is chance). Overall rates ran 37.5 to 48.2 across methods, with GPTs closest to chance. The targets are ordinary living people, not fictional characters, and the task is human-versus-machine, not which character said a line, so S52's negative narrows rather than falls. No agreement statistic was found, and LLM judges were scored against ground truth, not validated against the human raters; they picked the LLM answer as the human one above 90% for the two GPT-4 judges. · locator: candidate evidence excerpts · source: https://arxiv.org/abs/2404.13957

Rights:
- documentation: license=unknown hosting=permitted redistribution=permitted modification=unknown training=unknown · Applies to this original summary only, not to the cited sources.

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