SiNFluD: Creating and Evaluating Figurative Language Dataset for Sindhi

Low-resource languages such as Sinhala are often overlooked by open-source Large Language Models (LLMs). In this research, we extend an existing multilingual LLM (Llama-3-8B) to better serve Sinhala. We enhance the LLM tokenizer with Sinhala specific vocabulary and perform continual pre-training on a cleaned 10 million Sinhala corpus, resulting in the SinLlama model. This is the very first decoder-based open-source LLM with explicit Sinhala support. When SinLlama was instruction fine-tuned for three text classification tasks, it outperformed base and instruct variants of Llama-3-8B by a significant margin.

We present VIVID (Vietnamese Idioms for Validation and Interpretation Depth), the first systematic benchmark for evaluating culturally grounded figurative language understanding in Vietnamese. VIVID comprises 1,636 idioms and proverbs annotated with five complexity traits (literal expressions, pragmatic nuances, Sino-Vietnamese terms, uncommon vocabulary, folk knowledge) and seven semantic themes. We establish an evaluation framework combining generative and discriminative tasks, proposing an LLM-as-a-Judge approach with aspect-based prompting validated against human judgment (Cohen's kappa = 0.792). Evaluating eight state-of-the-art models reveals critical gaps: Vietnamese-specialized models drastically underperform multilingual systems (VinaLLaMA-7B: 0.13 vs. GPT-4o: 2.46), and even top models achieve less than 50% of maximum scores. Notably, few-shot prompting does not universally improve performance, with GPT-4o exhibiting degradation due to stylistic overfitting. Our analysis exposes systematic failures including literal over-interpretation, lexical gaps, and pragmatic flattening, demonstrating that current models lack cultural competence for nuanced figurative interpretation. VIVID provides an essential tool for advancing figurative language understanding in culturally rich contexts.

Although multilingual approaches to figurative language identification are not new, the shift beyond language homogeneous training data requires a clearer understanding of the contribution of translated multilingual supervision. We examine this question using 742 proverb concepts across 6,787 translated instances in seven languages. We evaluate five models, including multilingual encoders and instruction tuned LLMs, under progressively increasing levels of multilingual supervision. Moreover, we introduce a multidimensional annotation framework for proverbs that characterizes them through four complementary figurative forms: Metaphorical, Moral/Advisory, Cause-Effect, and Culture Specific. Our findings show that approximately 50% of the translated multilingual training data is sufficient to achieve near-optimal figurative language identification performance. We further show that combining diverse figurative forms yields the strongest overall performance. A notable finding is that the least frequent figurative form, Culture Specific, exhibits the largest performance gains under multilingual supervision. Furthermore, the Moral/Advisory and Culture Specific forms contribute most to the performance of instruction-tuned LLMs on figurative language identification. These findings motivate multilingual figurative language identification to move beyond metaphor-centric taxonomies toward concept level multidimensional frameworks that explicitly model complementary forms of figurative meaning.