COILD is an Indic‑centric parallel corpus that contains over 1.16 million human‑translated and verified sentence pairs across 20 Indian language pairs from four language families. The corpus is sourced from original Indian language materials in eight domains, and a 2,000‑sentence domain‑centric benchmark is provided for consistent multilingual evaluation. Experiments with IndicTrans2‑Distilled and NLLB‑200 show consistent improvements in automatic metrics and human judgments, underscoring the value of high‑quality Indic‑centric data.
By Kshetrimayum Boynao Singh, Nitin Kumar Mishra, Palash Pratim Dutta, Atai Waris Khan, Aparna Kaushik, Avinash Kumar, Deeksha, Deepak Kumar, Saroj Kumar Jha, Saloka Sengupta, Anansa Roy, Umalatha Kannoth, Saifulla Samar, Meena Sharma, Manpreet Kaur, Jyoti Sharma, Ashwini Vaidya, Muralikrishna SN, Md Shad Akhtar, Poonam Bansal, Amita Dev, Sanasam Ranbir Singh, Samit Bhattacharya, Tanmoy Chakraborty, Asif Ekbal
arXiv:2608. 15535v1 Announce Type: cross Abstract: We present L3Cube-IndicQuest v2, a large-scale gold-standard multilingual question-answering benchmark for evaluating the India-specific factual knowledge of Large Language Models (LLMs).
By Rinit Jain, Tirthraj Mahajan, Advait Joshi, Raviraj Joshi
arXiv:2605.29637v2 Announce Type: replace
Abstract: Large language models often exhibit a substantial gap between their performance in English and in lower-resourced languages on equivalent knowledge...
By Debajyoti Mazumder, Divyansh Pathak, Prashant Kodali, Aditya Joshi, Akshay Agarwal, Jasabanta Patro
arXiv:2608. 03095v1 Announce Type: cross Abstract: We present VIVID (Vietnamese Idioms for Validation and Interpretation Depth), the first systematic benchmark for evaluating culturally grounded figurative language understanding in Vietnamese.
By Tu Tran Do, Nhat Ngoc Nguyen, Khanh-Tung Tran, Hoang D. Nguyen, Tu Minh Phuong, Long Hoang Dang
The paper introduces a new benchmark for Urdu‑to‑English idiomatic translation, featuring 4,000 manually verified sentence pairs in both native Perso‑Arabic script and Romanized Urdu. It evaluates multiple tasks—translation, paraphrasing, idiom span detection, and back‑translation—using various prompting strategies, and finds that state‑of‑the‑art large language models outperform traditional neural machine translation systems, especially in preserving figurative meaning. The study also highlights challenges posed by the lack of standardized orthography in Romanized Urdu, which affects consistency and idiom span detection.
By Muhammad Farmal Khan, Mousumi Akter
arXiv:2608.28645v1 Announce Type: cross
Abstract: Low-resource languages without an adequate training corpus often use a related, higher-resource language as a scaffold for comprehension. Still, ther...
By Sindhu Shetty, Spurthi Setty, Natan Vidra
arXiv:2608. 03803v1 Announce Type: cross Abstract: Multilingual language models are deployed across a hundred or more languages, yet most benchmarks test whether a model can perform a task _in_ a language rather than whether it commands the language itself, conflating fluency with proficiency.
By Tom\'a\v{s} Burkert, Angelika Peljak-{\L}api\'nska, David Zelen\'y
The paper introduces a three‑layer checklist-and-judge framework to evaluate interpreter agents that mediate live conversation across languages. It assesses semantic, pragmatic, and cultural‑social dimensions—naturalness, intent, and social appropriateness—rather than just fidelity, in both single‑turn and multi‑turn settings. Extensive validation shows that conventional MT metrics miss failures in stronger interpreters, and that context, structured instructions, and cultural cues influence communicative success.
By Faiz Ghifari Haznitrama, Alice Oh
The paper introduces IndicReStruct, a benchmark dataset comprising two variants—GSM8K-Reordered and GSM8K-Voice—derived from GSM8K to test multilingual LLMs on Hindi and Malayalam. It evaluates six state‑of‑the‑art LLMs under constrained constituent reordering and active‑passive voice transformation, finding consistent and significant drops in mathematical reasoning performance. Qualitative error analysis and residual‑stream activation patching reveal that failures often stem from disrupted entity‑quantity alignment, with intermediate transformer layers playing a key role in restoring reasoning.
By Karthika Nhayakkat, Rajat Verma, Maharaj Brahma, Vetcha Gnana Mahesh, Maunendra Sankar Desarkar, Ganesh Ramakrishnan, Rohit Saluja
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:2608. 14896v1 Announce Type: cross Abstract: Large language models work well on English and behave in poorly understood ways on languages typologically far from it.
By Florian Braun
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