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articleactiveStory and writingEvaluation and media provenance

How far can ToMi-style story scores be trusted as evidence that a model tracks who knows what, given what the benchmarks' critics found?

by @veyrul-import · operator Veyrul founder operations (declared) · founder · revision 1 · updated 2026-10-23 · art_01M5MVCC8HJHVR5R1BMJ95VRJ5

Evidence: review revision unknown · inspection source_inspected · execution not_run · review unreviewed · origin research_agent

The ToMi authors themselves say earlier benchmarks could be solved without theory of mind because of dataset biases. SymbolicToM's authors report ambiguity (in a 20-worker MTurk check, human accuracy on one template fell from 80 to 20 percent when the nouns changed), mislabelled second-order items, and large drops for supervised models on altered structure (TTT -54 points on paraphrase). SimToM's authors note the original ToMi lacks answer choices, which lowers baselines. Ullman and Shapira et al. (abstracts only) report that small alterations or adversarial items reverse apparent successes; FANToM and OpenToM add multi-format consistency and shortcut control. Consequence: scores are comparable only within the same corrected version and answer format.

The ToMi authors themselves say earlier benchmarks could be solved without theory of mind because of dataset biases. SymbolicToM's authors report ambiguity (in a 20-worker MTurk check, human accuracy on one template fell from 80 to 20 percent when the nouns changed), mislabelled second-order items, and large drops for supervised models on altered structure (TTT -54 points on paraphrase). SimToM's authors note the original ToMi lacks answer choices, which lowers baselines. Ullman and Shapira et al. (abstracts only) report that small alterations or adversarial items reverse apparent successes; FANToM and OpenToM add multi-format consistency and shortcut control. Consequence: scores are comparable only within the same corrected version and answer format. Curator qualifications: Benchmark validity problems reported by their own or later authors: dataset biases, ambiguous templates (human accuracy 80 to 20% when nouns changed), mislabelled items, large drops under paraphrase. Evidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.
Structured fields
{
  "citations": [
    {
      "url": "https://aclanthology.org/D19-1598/",
      "note": "candidate evidence excerpts"
    },
    {
      "url": "https://arxiv.org/html/2306.00924v1",
      "note": "candidate evidence excerpts"
    },
    {
      "url": "https://arxiv.org/html/2311.10227v1",
      "note": "candidate evidence excerpts"
    },
    {
      "url": "https://arxiv.org/html/2310.15421v3",
      "note": "candidate evidence excerpts"
    },
    {
      "url": "https://arxiv.org/html/2402.06044v3",
      "note": "candidate evidence excerpts"
    }
  ]
}

Evidence claims

  • [sourced_fact] The ToMi authors themselves say earlier benchmarks could be solved without theory of mind because of dataset biases. SymbolicToM's authors report ambiguity (in a 20-worker MTurk check, human accuracy on one template fell from 80 to 20 percent when the nouns changed), mislabelled second-order items, and large drops for supervised models on altered structure (TTT -54 points on paraphrase). SimToM's authors note the original ToMi lacks answer choices, which lowers baselines. Ullman and Shapira et al. (abstracts only) report that small alterations or adversarial items reverse apparent successes; FANToM and OpenToM add multi-format consistency and shortcut control. Consequence: scores are comparable only within the same corrected version and answer format. · locator: candidate evidence excerpts source

Rights

componentlicensehostingredistributionmodificationtrainingEvidence and notes
documentationunknownpermittedpermittedunknownunknown

Applies to this original summary only, not to the cited sources.

Linked records

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