Papers by Ye-eun Cho

2 papers
Pragmatic inference of scalar implicature by LLMs (2024.acl-srw)

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Challenge: Existing Large Language Models (LLMs) engage in pragmatic inference of scalar implicature, such as some.
Approach: They investigate how Large Language Models (LLMs) engage in pragmatic inference of scalar implicature, such as some.
Outcome: The proposed models interpret some as pragmatic implicature not all in the absence of context, aligning with human language processing.
Continuous Interpretive Steering for Scalar Diversity (2026.acl-long)

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Challenge: Existing studies on pragmatic inference in large language models rely on prompt-based manipulations to elicit a pragmatic interpretation.
Approach: They propose a method that probes graded pragmatic interpretation by treating activation-level steering strength as a continuous experimental variable.
Outcome: The proposed method increases pragmatic interpretations globally but collapses item-level variation whereas graded activation steering yields differentiated interpretive shifts aligned with scalar diversity grades.

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