“Vorbești Românește?” A Recipe to Train Powerful Romanian LLMs with English Instructions (2024.findings-emnlp)
Copied to clipboard
Mihai Masala, Denis Ilie-Ablachim, Alexandru Dima, Dragos Georgian Corlatescu, Miruna-Andreea Zavelca, Ovio Olaru, Simina-Maria Terian, Andrei Terian, Marius Leordeanu, Horia Velicu, Marius Popescu, Mihai Dascalu, Traian Rebedea
| Challenge: | Large Language Models (LLMs) have achieved almost human-like performance on various tasks. |
| Approach: | They are the first to collect and translate a large collection of texts, instructions, and benchmarks and train, evaluate and release open-source LLMs tailored for Romanian. |
| Outcome: | The proposed model trains, evaluates and releases open-source models tailored for Romanian. |
Similar Papers
LLMs for Low Resource Languages in Multilingual, Multimodal and Dialectal Settings (2024.eacl-tutorials)
Copied to clipboard
| Challenge: | Recent advances in AI can be attributed to the remarkable performance of Large Language Models (LLMs) success of LLMs depends on specific training techniques, such as instruction tuning and prompting . |
| Approach: | They explore the capabilities of Large Language Models (LLMs) in various tasks and languages . they also examine their performance, fine-tuning, instructions tuning, and close vs. open models . |
| Outcome: | The proposed model can be used for speech and multimodal tasks across modalities, languages, and dialects. |
RoCode: A Dataset for Measuring Code Intelligence from Problem Definitions in Romanian (2024.lrec-main)
Copied to clipboard
| Challenge: | Large language models are capable of solving tasks in natural language, but most tests assume they are written in English. |
| Approach: | They propose to use a dataset to measure the generalization power of large language models in a language other than English to evaluate their code intelligence. |
| Outcome: | The proposed dataset provides a benchmark for evaluating the code intelligence of language models trained on Romanian / multilingual text and a fine-tuning set for pretrained Romanian models. |
Data and Model Centric Approaches for Expansion of Large Language Models to New languages (2025.emnlp-tutorials)
Copied to clipboard
| Challenge: | Existing LLMs mainly support English alongside a handful of high resource languages . this leaves a major gap for most low-resource languages despite increasing pace of research . |
| Approach: | This tutorial examines approaches to expand the language coverage of LLMs . they look at tokenizer training, pre-training, instruction tuning, alignment, evaluation, etc. |
| Outcome: | This tutorial examines approaches to expand the language coverage of LLMs . it provides an efficient and viable path to bring LLM technologies to low-resource languages . |
DanteLLM: Let’s Push Italian LLM Research Forward! (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing models for large language processing in the English language are limited in resources and evaluation tools for non-English languages. |
| Approach: | They propose a benchmark and an open LLM Leaderboard to evaluate LLMs’ performance in Italian and propose 'DanteLLM' it is the most performant LLM in the world, with improvements of up to 6 points . |
| Outcome: | The proposed model outperforms existing models in Italian and offers a blueprint for the development and evaluation of LLMs in other languages. |
Benchmarking and Improving Long-Text Translation with Large Language Models (2024.findings-acl)
Copied to clipboard
Longyue Wang, Zefeng Du, Wenxiang Jiao, Chenyang Lyu, Jianhui Pang, Leyang Cui, Kaiqiang Song, Derek Wong, Shuming Shi, Zhaopeng Tu
| Challenge: | Recent studies have illuminated the promising capabilities of large language models (LLMs) in handling long texts. |
| Approach: | They construct a benchmark dataset specifically designed for the finetuning and evaluation of large language models (LLMs) they compare LLMs with MT models and find they exhibit shortcomings in long-text domains . |
| Outcome: | The proposed model performs better in long-text translation, and its performance diminishes as document size increases. |
Do Large Language Models have an English Accent? Evaluating and Improving the Naturalness of Multilingual LLMs (2025.acl-long)
Copied to clipboard
| Challenge: | Current Large Language Models (LLMs) are predominantly designed with English as the primary language, but many are still English-dominated. |
| Approach: | They propose to use automatic corpus-level metrics to assess lexical and syntactic naturalness of LLMs in a multilingual context. |
| Outcome: | The proposed method improves naturalness of LLMs in target languages without compromising performance on general-purpose benchmarks. |
BenchMAX: A Comprehensive Multilingual Evaluation Suite for Large Language Models (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing multilingual benchmarks focus primarily on language understanding tasks. |
| Approach: | They develop a multi-way multilingual benchmark that measures critical capabilities of large language models across languages. |
| Outcome: | Extensive experiments on BenchMAX reveal uneven utilization of core capabilities across languages, emphasizing the performance gaps that scaling model size alone does not resolve. |
LLM-Powered Benchmark Factory: Reliable, Generic, and Efficient (2026.acl-long)
Copied to clipboard
Peiwen Yuan, Shaoxiong Feng, Yiwei Li, Xinglin Wang, Yueqi Zhang, Jiayi Shi, Chuyi Tan, Boyuan Pan, Yao Hu, Kan Li
| Challenge: | Using generic and efficient benchmark generators, human annotators are limited by inefficiency . current benchmark generator methods rely on seed signals, leading to long cycles and high costs . |
| Approach: | They propose a framework to evaluate LLMs as generic benchmark generators and integrate them as BenchMaker. |
| Outcome: | The proposed framework achieves comparable performance to human-annotated benchmarks on most metrics. |
Adaptation of Large Language Models (2025.naacl-tutorial)
Copied to clipboard
| Challenge: | a tutorial on adaptation of large language models addresses the growing demand for models that go beyond static capabilities. |
| Approach: | This tutorial will provide an overview of dynamic, domain-specific, and task-adaptive LLM adaptation techniques. |
| Outcome: | This tutorial will outline dynamic, domain-specific, and task-adaptive LLM adaptation techniques. |
Ready to Translate, Not to Represent? Bias and Performance Gaps in Multilingual LLMs Across Language Families and Domains (2026.findings-acl)
Copied to clipboard
Md. Faiyaz Abdullah Sayeedi, Subhey Sadi Rahman, Md. Mahbub Alam, Md. Adnanul Islam, Jannatul Ferdous Deepti, Tasnim Mohiuddin, Md Mofijul Islam, Swakkhar Shatabda
| Challenge: | Large Language Models (LLMs) have redefined Machine Translation, enabling context-aware and fluent translations across hundreds of languages and textual domains. |
| Approach: | They propose a framework and dataset to evaluate the translation quality and fairness of open-source LLMs. |
| Outcome: | The proposed framework and dataset evaluates translation quality and fairness of open-source LLMs. |