Introduces MalayPrag benchmark and five pragmatic attributes, showing LLMs struggle to link discourse particles to functions in Malay but improve with attribute scaffolding.
Pub: A pragmatics understanding benchmark for assessing llms’ pragmatics capabilities
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CL 2verdicts
UNVERDICTED 2representative citing papers
Using prompts that incorporate implicature leads to responses that humans prefer 67.6% of the time over literal prompts, with larger models better at inferring intent.
citing papers explorer
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Can Large Language Models Handle Discourse Particles? A Case Study of Colloquial Malay
Introduces MalayPrag benchmark and five pragmatic attributes, showing LLMs struggle to link discourse particles to functions in Malay but improve with attribute scaffolding.
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Implicature in Interaction: Understanding Implicature Improves Alignment in Human-LLM Interaction
Using prompts that incorporate implicature leads to responses that humans prefer 67.6% of the time over literal prompts, with larger models better at inferring intent.