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Computer Science > Computation and Language

arXiv:2506.11440 (cs)
[Submitted on 13 Jun 2025]

Title:AbsenceBench: Language Models Can't Tell What's Missing

Authors:Harvey Yiyun Fu, Aryan Shrivastava, Jared Moore, Peter West,
Chenhao Tan, Ari Holtzman
View a PDF of the paper titled AbsenceBench: Language Models Can't
Tell What's Missing, by Harvey Yiyun Fu and 5 other authors
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    Abstract:Large language models (LLMs) are increasingly capable of
    processing long inputs and locating specific information within
    them, as evidenced by their performance on the Needle in a
    Haystack (NIAH) test. However, while models excel at recalling
    surprising information, they still struggle to identify clearly
    omitted information. We introduce AbsenceBench to assesses LLMs'
    capacity to detect missing information across three domains:
    numerical sequences, poetry, and GitHub pull requests.
    AbsenceBench asks models to identify which pieces of a document
    were deliberately removed, given access to both the original and
    edited contexts. Despite the apparent straightforwardness of
    these tasks, our experiments reveal that even state-of-the-art
    models like Claude-3.7-Sonnet achieve only 69.6% F1-score with a
    modest average context length of 5K tokens. Our analysis suggests
    this poor performance stems from a fundamental limitation:
    Transformer attention mechanisms cannot easily attend to "gaps"
    in documents since these absences don't correspond to any
    specific keys that can be attended to. Overall, our results and
    analysis provide a case study of the close proximity of tasks
    where models are already superhuman (NIAH) and tasks where models
    breakdown unexpectedly (AbsenceBench).

Comments: 23 pages, 8 figures. Code and data are publicly available
          at this https URL
Subjects: Computation and Language (cs.CL)
Cite as:  arXiv:2506.11440 [cs.CL]
          (or arXiv:2506.11440v1 [cs.CL] for this version)
          https://doi.org/10.48550/arXiv.2506.11440
          Focus to learn more
          arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Harvey Yiyun Fu [view email]
[v1] Fri, 13 Jun 2025 03:38:29 UTC (5,538 KB)
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