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When a Name Is Not a Name: A Benchmark Dataset and Distilled Reasoning for Culturally Entangled Bangla Homographs in Low-Resource LLMs

arXiv cs.CL · 2026-07-21 Cached

This paper introduces a benchmark dataset of 1,516 expert-verified Bangla sentences for disambiguating culturally entangled homographs (words that are both names and common nouns). It shows that LLMs suffer from dominant-meaning bias and proposes contrastive chain-of-thought prompting and distillation to reduce this bias.

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