The study investigates how multilingual training affects figurative language identification in proverbs, using 742 proverb concepts translated into seven languages. Five models—including multilingual encoders and instruction‑tuned LLMs—were evaluated with varying levels of multilingual supervision, and a new multidimensional annotation framework was introduced to classify proverbs into metaphorical, moral/advisory, cause‑effect, and culture‑specific forms. Results show that adding multilingual data beyond 50% yields limited gains, but the best supervision level depends on the model and language; combining diverse figurative forms improves overall performance, especially for the least frequent culture‑specific form.
By Rama Alomair, Remas Alsubaie, Walaa Saifalislam, Rima Alsonbul, Mona Alnajjar, Razan Aldossari, Haya Alibrahim, Abeer Aldayel
arXiv:2606. 02147v1 Announce Type: cross Abstract: Idiomatic expressions pose a major challenge for multilingual NLP because their meanings shift between figurative and literal usage, often requiring context for accurate interpretation.
By Saeed Almheiri, Bilal Elbouardi, Salsabila Zahirah Pranida, Irina Nikishina, Ashwath Rao B, Parameswari Krishnamurthy, Muhammad Cendekia Airlangga, Rifo Ahmad Genadi, Nguyen Phan Gia Bao, Amir Hossein Yari, Hawau Olamide Toyin, Nurdaulet Mukhituly, Mena Attia, Besher Hassan, Ahmad Fathan Hidayatullah, Tatsuki Kuribayashi, Haonan Li, Suma Bhat, Fajri Koto
The paper introduces the first large‑scale benchmark dataset of Bangla idioms, along with a synthetic multiple‑choice question set for idiom meaning identification. It evaluates recent large language models on three idiom‑related tasks—paraphrasing, idiom span detection, and meaning identification—using zero‑shot and few‑shot prompting. Results show significant variability across models, with Phi‑4‑mini‑instruct best at paraphrasing, Kimi‑K2‑32b‑instruct excelling at span detection, and Gemini‑2.5‑flash leading in meaning identification.
By Mousumi Akter, Md. Faiyaz Abdullah Sayeedi, Nurul Labib Sayeedi, Swakkhar Shatabda
arXiv:2608. 04670v1 Announce Type: cross Abstract: Large Language Models (LLMs) have transformed computational linguistics and achieved remarkable performance across numerous natural language processing tasks, yet significant gaps persist in understanding how these systems process culturally embedded linguistic expressions.
By Enrico Mensa, Lorenzo Zane, Calogero Jerik Scozzaro, Matteo Delsanto, Tommaso Milani, Daniele Paolo Radicioni
VakyArth is the first pragmatic benchmark for Indic languages, covering Hindi, Punjabi, Tamil, and Malayalam. It tests models on five pragmatic phenomena—deixis, speech acts, implicature, social pragmatics, and coherence—using multiple-choice questions, natural language inference, and translation tasks authored by native speakers. Evaluation of multilingual LLMs shows consistent failures on pragmatic meanings rooted in Indic linguistic and cultural conventions, with systematic differences across languages and tasks.
By Usneek Singh, Poorvaja Veera Balaji Kumar, Parth Nanda, Anand Madhusoodanan, Geyang Guo, Wei Xu, Junyi Jessy L
arXiv:2609.18156v1 Announce Type: new
Abstract: Teochew has a substantial speaker community and exhibits distinctive lexical, syntactic, and pragmatic features, yet textual resources for evaluating l...
By Jianan Wu
arXiv:2606. 18922v1 Announce Type: cross Abstract: Figurative language and negation are two areas that challenge current language models, however, both are widely used throughout written and spoken language.
By Jasmine Owers, Edwin Simpson, Martha Lewis
Teochew has a substantial speaker community and exhibits distinctive lexical, syntactic, and pragmatic features, yet textual resources for evaluating large language models remain limited. We present T...
MetaHOPE is an error‑severity‑aware annotation framework designed to evaluate how well machine translation (MT) and large language models (LLMs) translate metaphors. The authors applied MetaHOPE to three state‑of‑the‑art systems—GoogleMT, GPT5.4, and Hunyuan‑7b—using two human‑annotated metaphor corpora (VUAMC and PSUCMC) for English‑to‑Chinese and Chinese‑to‑English translation. They also produced a bilingual post‑edited gold reference, creating a new resource for metaphor translation research.
By Jiahui Liang, Lifeng Han
arXiv:2510. 04120v2 Announce Type: replace-cross Abstract: Large language models (LLMs) achieve strong performance on metaphor detection and interpretation tasks, yet it remains unclear what such behavioral success reveals about metaphor processing.
By Fengying Ye, Shanshan Wang, Lidia S. Chao, Derek F. Wong
arXiv:2608.28405v1 Announce Type: new
Abstract: Current cultural evaluations for large language models (LLMs) often reduce culture to single-turn factual recall via MCQs, failing to capture a common...
By Bryan Chen Zhengyu Tan, Weihua Zheng, Thong T. Doan, Bich Ngoc Doan, Jia Wang Peh, Xiaoyuan Yi, Jing Yao, Xing Xie, Nancy F. Chen, Zhengyuan Liu, JinYeong Bak, Wafi Shamdi, Soo Kai Chie, Liew Yu Siong, Aina Azyyati Binti Mohamad Rezal, Lew Yan Yan Vanessa, Huadan Wu, Dylan Raharja, Nadya Yuki Wangsajaya, Akane Fukushige, Kazushi Kato, Koji Inoue, Tatsuya Kawahara, Jaehyung Seo, Dongjun Kim, Seungyoon Lee, Zi Haur Pang, Rui Yang Tan, Charibeth Ko Cheng, Maria Regina Justina Estuar, Jann Railey Montalan, Pham Minh Duc, Roy Ka-Wei Lee
The study compares four approaches for Chinese sentence-level metaphor identification: BERT fine‑tuning, QLoRA-based large language model fine‑tuning, zero‑shot LLM prompting, and zero‑shot prompting with an expert‑informed procedural Skill. Results show that fine‑tuning yields the highest accuracy on the native test set, while the Skill‑based zero‑shot method provides the most stable performance across three datasets, achieving the highest external floor and the smallest performance range. Adding the Skill reduces false positives on one dataset but increases false negatives on others, indicating a trade‑off between precision and recall.
By Yufeng Wu, Meichun Liu