When Words Don't Mean What They Say: Figurative Understanding in Bengali Idioms
- URL: http://arxiv.org/abs/2602.12921v1
- Date: Fri, 13 Feb 2026 13:26:11 GMT
- Title: When Words Don't Mean What They Say: Figurative Understanding in Bengali Idioms
- Authors: Adib Sakhawat, Shamim Ara Parveen, Md Ruhul Amin, Shamim Al Mahmud, Md Saiful Islam, Tahera Khatun,
- Abstract summary: Figurative language understanding remains a significant challenge for Large Language Models (LLMs)<n>We introduce a new idiom dataset, a large-scale, culturally-grounded corpus of 10,361 Bengali idioms.<n>Each idiom is annotated under a comprehensive 19-field schema, established and refined through a deliberative expert consensus process.<n>We evaluate 30 state-of-the-art multilingual and instruction-tuned LLMs on the task of inferring figurative meaning.
- Score: 1.5840067220859924
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Figurative language understanding remains a significant challenge for Large Language Models (LLMs), especially for low-resource languages. To address this, we introduce a new idiom dataset, a large-scale, culturally-grounded corpus of 10,361 Bengali idioms. Each idiom is annotated under a comprehensive 19-field schema, established and refined through a deliberative expert consensus process, that captures its semantic, syntactic, cultural, and religious dimensions, providing a rich, structured resource for computational linguistics. To establish a robust benchmark for Bangla figurative language understanding, we evaluate 30 state-of-the-art multilingual and instruction-tuned LLMs on the task of inferring figurative meaning. Our results reveal a critical performance gap, with no model surpassing 50% accuracy, a stark contrast to significantly higher human performance (83.4%). This underscores the limitations of existing models in cross-linguistic and cultural reasoning. By releasing the new idiom dataset and benchmark, we provide foundational infrastructure for advancing figurative language understanding and cultural grounding in LLMs for Bengali and other low-resource languages.
Related papers
- BengaliFig: A Low-Resource Challenge for Figurative and Culturally Grounded Reasoning in Bengali [0.0]
We present BengaliFig, a compact yet richly annotated challenge set.<n>The dataset contains 435 unique riddles drawn from Bengali oral and literary traditions.<n>Each item is annotated along five dimensions capturing reasoning type, trap type, cultural depth, answer category, and difficulty.
arXiv Detail & Related papers (2025-11-25T15:26:47Z) - From Polyester Girlfriends to Blind Mice: Creating the First Pragmatics Understanding Benchmarks for Slovene [0.12277343096128711]
We introduce SloPragEval and SloPragMega, the first pragmatics understanding benchmarks for Slovene.<n>We discuss the difficulties of translation, describe the campaign to establish a human baseline, and report pilot evaluations with LLMs.<n>Our results indicate that current models have greatly improved in understanding nuanced language but may still fail to infer implied speaker meaning in non-literal utterances.
arXiv Detail & Related papers (2025-10-24T15:43:42Z) - CRaFT: An Explanation-Based Framework for Evaluating Cultural Reasoning in Multilingual Language Models [0.42970700836450487]
We introduce CRaFT, an explanation-based multilingual evaluation framework designed to assess how large language models (LLMs) reason across cultural contexts.<n>We apply the framework to 50 culturally grounded questions from the World Values Survey, translated into Arabic, Bengali, and Spanish, and evaluate three models (GPT, DeepSeek, and FANAR) across over 2,100 answer-explanation pairs.<n>Results reveal significant cross-lingual variation in reasoning: Arabic reduces fluency, Bengali enhances it, and Spanish remains largely stable.
arXiv Detail & Related papers (2025-10-15T18:49:10Z) - Holmes: A Benchmark to Assess the Linguistic Competence of Language Models [59.627729608055006]
We introduce Holmes, a new benchmark designed to assess language models (LMs) linguistic competence.
We use computation-based probing to examine LMs' internal representations regarding distinct linguistic phenomena.
As a result, we meet recent calls to disentangle LMs' linguistic competence from other cognitive abilities.
arXiv Detail & Related papers (2024-04-29T17:58:36Z) - NusaWrites: Constructing High-Quality Corpora for Underrepresented and
Extremely Low-Resource Languages [54.808217147579036]
We conduct a case study on Indonesian local languages.
We compare the effectiveness of online scraping, human translation, and paragraph writing by native speakers in constructing datasets.
Our findings demonstrate that datasets generated through paragraph writing by native speakers exhibit superior quality in terms of lexical diversity and cultural content.
arXiv Detail & Related papers (2023-09-19T14:42:33Z) - Multi-lingual and Multi-cultural Figurative Language Understanding [69.47641938200817]
Figurative language permeates human communication, but is relatively understudied in NLP.
We create a dataset for seven diverse languages associated with a variety of cultures: Hindi, Indonesian, Javanese, Kannada, Sundanese, Swahili and Yoruba.
Our dataset reveals that each language relies on cultural and regional concepts for figurative expressions, with the highest overlap between languages originating from the same region.
All languages exhibit a significant deficiency compared to English, with variations in performance reflecting the availability of pre-training and fine-tuning data.
arXiv Detail & Related papers (2023-05-25T15:30:31Z) - CLSE: Corpus of Linguistically Significant Entities [58.29901964387952]
We release a Corpus of Linguistically Significant Entities (CLSE) annotated by experts.
CLSE covers 74 different semantic types to support various applications from airline ticketing to video games.
We create a linguistically representative NLG evaluation benchmark in three languages: French, Marathi, and Russian.
arXiv Detail & Related papers (2022-11-04T12:56:12Z) - Transparency Helps Reveal When Language Models Learn Meaning [71.96920839263457]
Our systematic experiments with synthetic data reveal that, with languages where all expressions have context-independent denotations, both autoregressive and masked language models learn to emulate semantic relations between expressions.
Turning to natural language, our experiments with a specific phenomenon -- referential opacity -- add to the growing body of evidence that current language models do not well-represent natural language semantics.
arXiv Detail & Related papers (2022-10-14T02:35:19Z) - Testing the Ability of Language Models to Interpret Figurative Language [69.59943454934799]
Figurative and metaphorical language are commonplace in discourse.
It remains an open question to what extent modern language models can interpret nonliteral phrases.
We introduce Fig-QA, a Winograd-style nonliteral language understanding task.
arXiv Detail & Related papers (2022-04-26T23:42:22Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.