# Does tracking where a character thinks an object is carry over to what the character feels about an event they partly saw, and does a perspective-filter prompt help?

- id: art_01M5MVCAY6N1E65A27S2PRMGMS
- 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: 2026-10-23T17:01:48.864Z
- url: https://veyrul.com/articles/art_01M5MVCAY6N1E65A27S2PRMGMS

OpenToM (arXiv 2402.06044v3) has 696 narratives (596 GPT-3.5-Turbo, 100 longer GPT-4-Turbo) with characters given personality traits and intention-driven actions, and 23 first- and second-order questions each: location (coarse and fine), multi-hop (container fullness, accessibility) and attitude of an observer to the mover's action. GPT-4-Turbo led most columns but scored 0.544 macro-F1 on attitude against 0.862 for humans, and chain-of-thought barely helped attitude. SimToM (perspective filter first, then answer) was mixed on OpenToM: gains on coarse location for the GPT models, mixed elsewhere. Models of 2023-2024; zero-shot only.

OpenToM (arXiv 2402.06044v3) has 696 narratives (596 GPT-3.5-Turbo, 100 longer GPT-4-Turbo) with characters given personality traits and intention-driven actions, and 23 first- and second-order questions each: location (coarse and fine), multi-hop (container fullness, accessibility) and attitude of an observer to the mover's action. GPT-4-Turbo led most columns but scored 0.544 macro-F1 on attitude against 0.862 for humans, and chain-of-thought barely helped attitude. SimToM (perspective filter first, then answer) was mixed on OpenToM: gains on coarse location for the GPT models, mixed elsewhere. Models of 2023-2024; zero-shot only.

Curator qualifications: OpenToM: 696 narratives with personality-driven actions; GPT-4-Turbo weak on attitude questions (0.544 macro-F1).

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/2402.06044v3",
      "note": "candidate evidence excerpts"
    }
  ]
}
```

Evidence claims:
- [sourced_fact] OpenToM (arXiv 2402.06044v3) has 696 narratives (596 GPT-3.5-Turbo, 100 longer GPT-4-Turbo) with characters given personality traits and intention-driven actions, and 23 first- and second-order questions each: location (coarse and fine), multi-hop (container fullness, accessibility) and attitude of an observer to the mover's action. GPT-4-Turbo led most columns but scored 0.544 macro-F1 on attitude against 0.862 for humans, and chain-of-thought barely helped attitude. SimToM (perspective filter first, then answer) was mixed on OpenToM: gains on coarse location for the GPT models, mixed elsewhere. Models of 2023-2024; zero-shot only. · locator: candidate evidence excerpts · source: https://arxiv.org/html/2402.06044v3

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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