# Is there an automatic measure of whether a model makes different characters sound different from each other, and what did it show?

- id: art_01M5MVAZ7Q74BVNV549EG10TCV
- 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, T18, T20 · tags: none
- updated: 2026-10-23T17:01:04.112Z
- url: https://veyrul.com/articles/art_01M5MVAZ7Q74BVNV549EG10TCV

Speaker Verification in Agent-Generated Conversations (arXiv 2405.10150v2) defines a Distinction Score: embed lines the same model generated for two different roles and take one minus their cosine similarity. In self-play with RoleBench roles, every role-play system scored below the real-utterance value (72.91): from 63.57 (LLaMA2-Chat-7B) to 38.98 (CharacterLLM); the authors conclude that generated lines are similar across role settings and suggest pretraining style persists. The measure rests on verifiers validated on human-written text; the generated-line scores were not compared with human ratings in the passages read.

Speaker Verification in Agent-Generated Conversations (arXiv 2405.10150v2) defines a Distinction Score: embed lines the same model generated for two different roles and take one minus their cosine similarity. In self-play with RoleBench roles, every role-play system scored below the real-utterance value (72.91): from 63.57 (LLaMA2-Chat-7B) to 38.98 (CharacterLLM); the authors conclude that generated lines are similar across role settings and suggest pretraining style persists. The measure rests on verifiers validated on human-written text; the generated-line scores were not compared with human ratings in the passages read.

Curator qualifications: Every tested role-play system scored below real utterances on Distinction (72.91), from 63.57 down to 38.98: generated lines for different roles are more alike than real ones. Verifier validated on human text only.

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/html/2405.10150v2",
      "note": "arXiv 2405.10150v2 HTML Table 3 and speaker-verification table"
    },
    {
      "url": "https://raw.githubusercontent.com/IzzetYoung/AGCSpeakerVerification/9215f365fffd6697244e7e15d4548d60dc6cde51/README.md",
      "note": "arXiv 2405.10150v2 HTML Table 3 and speaker-verification table"
    }
  ]
}
```

Evidence claims:
- [sourced_fact] Speaker Verification in Agent-Generated Conversations (arXiv 2405.10150v2) defines a Distinction Score: embed lines the same model generated for two different roles and take one minus their cosine similarity. In self-play with RoleBench roles, every role-play system scored below the real-utterance value (72.91): from 63.57 (LLaMA2-Chat-7B) to 38.98 (CharacterLLM); the authors conclude that generated lines are similar across role settings and suggest pretraining style persists. The measure rests on verifiers validated on human-written text; the generated-line scores were not compared with human ratings in the passages read. · locator: arXiv 2405.10150v2 HTML Table 3 and speaker-verification table · source: https://arxiv.org/html/2405.10150v2

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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