Dissecting the Ullman Variations with a SCALPEL: Why do LLMs fail at Trivial Alterations to the False Belief Task?
<|reference_start|>Dissecting the Ullman Variations with a SCALPEL: Why do LLMs fail at Trivial Alterations to the False Belief Task?: Recent empirical results have sparked a debate about whether or not Large Language Models (LLMs) are capable of Theory of Mind (ToM). While some have found LLMs to be successful on ToM evaluations such as the False Belief task (Kosinski, 2023), others have argued that LLMs solve these tasks by exploiting spurious correlations -- not representing beliefs -- since they fail on trivial alterations to these tasks (Ullman, 2023). In this paper, we introduce SCALPEL: a technique to generate targeted modifications for False Belief tasks to test different specific hypotheses about why LLMs fail. We find that modifications which make explicit common inferences -- such as that looking at a transparent object implies recognizing its contents -- preserve LLMs' performance. This suggests that LLMs' failures on modified ToM tasks could result from a lack of more general commonsense reasoning, rather than a failure to represent mental states. We argue that SCALPEL could be helpful for explaining LLM successes and failures in other cases.<|reference_end|>
@article{pi2024dissecting,
    title={Dissecting the Ullman Variations with a SCALPEL: Why do LLMs fail at
  Trivial Alterations to the False Belief Task?},
    author={Zhiqiang Pi, Annapurna Vadaparty, Benjamin K. Bergen, and Cameron R.
  Jones},
    journal={arXiv preprint arXiv:2406.14737},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.14737},
    primaryClass={cs.CL}
}