Papers by Suet-Ying Lam

2 papers
Leveraging Human Production-Interpretation Asymmetries to Test LLM Cognitive Plausibility (2025.acl-short)

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Challenge: Existing research on the linguistic capabilities of large language models has focused on their performance in language interpretation.
Approach: They examine whether large language models (LLMs) process language similarly to humans . they use an empirically documented asymmetry between production and interpretation in humans a testbed .
Outcome: The proposed model can replicate human-like distinctions between production and interpretation.
Large Language Models Are Partially Primed in Pronoun Interpretation (2023.findings-acl)

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Challenge: Existing studies suggest large language models acquire rich linguistic representations, but little is known about whether they adapt to linguistic biases in a human-like way.
Approach: They examine whether large language models display human-like referential biases using stimuli and procedures from real psycholinguistic experiments.
Outcome: The proposed models display human-like referential biases when exposed to referential patterns in the local context.

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