Papers by Andreas Schramm

3 papers
How LLMs Comprehend Temporal Meaning in Narratives: A Case Study in Cognitive Evaluation of LLMs (2025.acl-long)

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Challenge: Large language models exhibit increasingly sophisticated linguistic capabilities, yet the extent to which these models reflect human-like cognition versus advanced pattern recognition remains an open question.
Approach: They conduct a series of targeted experiments to assess whether LLMs construct semantic representations and pragmatic inferences in a human-like manner.
Outcome: The proposed framework can be used to assess the cognitive and linguistic capabilities of large language models (LLMs).
Strong Memory, Weak Control: An Empirical Study of Executive Functioning in LLMs (2026.eacl-long)

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Challenge: Working memory is a critical component of human intelligence and executive functioning . it is correlated with performance on various cognitive tasks, including fluid intelligence .
Approach: They apply working memory tasks to large language models to estimate working memory capacity . they find that LLMs exceed normative human scores, but not executive functioning benchmarks .
Outcome: The proposed models do not show higher performance on executive functioning tasks or problem solving benchmarks.
Mary, the Cheeseburger-Eating Vegetarian: Do LLMs Recognize Incoherence in Narratives? (2026.eacl-long)

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Challenge: Contemporary models of (human) reading comprehension characterize comprehension as a dynamic process in which the reader continually builds and updates representations to maintain coherence and integrate new information with prior knowledge.
Approach: They use a paired narrative dataset to examine the extent to which large language models can reliably separate incoherent and coherent stories.
Outcome: The proposed models do not eliminate the deficits in the model internal state and behavior.

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