Setting Standards in Turkish NLP: TR-MMLU for Large Language Model Evaluation

• URL: http://arxiv.org/abs/2501.00593v2
• Date: Sat, 04 Jan 2025 20:42:33 GMT
• Title: Setting Standards in Turkish NLP: TR-MMLU for Large Language Model Evaluation
• Authors: M. Ali Bayram, Ali Arda Fincan, Ahmet Semih Gümüş, Banu Diri, Savaş Yıldırım, Öner Aytaş,
• Abstract summary: The Turkish MMLU (TR-MMLU) benchmark is designed to assess the linguistic and conceptual capabilities of large language models (LLMs) in Turkish. TR-MMLU is constructed from a dataset comprising 6200 multiple-choice questions across 62 sections, selected from a pool of 280000 questions spanning 67 disciplines and over 800 topics within the Turkish education system. Our findings reveal critical challenges, such as the impact of tokenization and fine-tuning strategies, and highlight areas for improvement in model design.
• Score: 0.29687381456163997
• License:
• Abstract: Language models have made remarkable advancements in understanding and generating human language, achieving notable success across a wide array of applications. However, evaluating these models remains a significant challenge, particularly for resource-limited languages such as Turkish. To address this gap, we introduce the Turkish MMLU (TR-MMLU) benchmark, a comprehensive evaluation framework designed to assess the linguistic and conceptual capabilities of large language models (LLMs) in Turkish. TR-MMLU is constructed from a carefully curated dataset comprising 6200 multiple-choice questions across 62 sections, selected from a pool of 280000 questions spanning 67 disciplines and over 800 topics within the Turkish education system. This benchmark provides a transparent, reproducible, and culturally relevant tool for evaluating model performance. It serves as a standard framework for Turkish NLP research, enabling detailed analyses of LLMs' capabilities in processing Turkish text and fostering the development of more robust and accurate language models. In this study, we evaluate state-of-the-art LLMs on TR-MMLU, providing insights into their strengths and limitations for Turkish-specific tasks. Our findings reveal critical challenges, such as the impact of tokenization and fine-tuning strategies, and highlight areas for improvement in model design. By setting a new standard for evaluating Turkish language models, TR-MMLU aims to inspire future innovations and support the advancement of Turkish NLP research.

Related papers

• TUMLU: A Unified and Native Language Understanding Benchmark for Turkic Languages [2.115206401188031]
  We propose two benchmarks for Turkic language MMLU: TUMLU and TUMLU-mini. TUMLU-mini consists of middle- and high-school level questions spanning 11 academic subjects in Azerbaijani, Crimean Tatar, Karakalpak, Kazakh, Tatar, Turkish, Uyghur, and Uzbek. We also present TUMLU-mini, a more concise, balanced, and manually verified subset of the dataset.
  arXiv  Detail & Related papers  (2025-02-16T07:07:38Z)
• Optimizing Large Language Models for Turkish: New Methodologies in Corpus Selection and Training [0.0]
  We adapt Large Language Model generated datasets and translated English datasets into Turkish. This approach led to substantial enhancements in model accuracy for both few-shot and zero-shot learning scenarios.
  arXiv  Detail & Related papers  (2024-12-03T19:17:18Z)
• All Languages Matter: Evaluating LMMs on Culturally Diverse 100 Languages [73.93600813999306]
  ALM-bench is the largest and most comprehensive effort to date for evaluating LMMs across 100 languages. It challenges existing models by testing their ability to understand and reason about culturally diverse images paired with text in various languages. The benchmark offers a robust and nuanced evaluation framework featuring various question formats, including true/false, multiple choice, and open-ended questions.
  arXiv  Detail & Related papers  (2024-11-25T15:44:42Z)
• TurkishMMLU: Measuring Massive Multitask Language Understanding in Turkish [54.51310112013655]
  We introduce the first multitask, multiple-choice Turkish QA benchmark, TurkishMMLU. TurkishMMLU includes over 10,000 questions, covering 9 different subjects from Turkish high-school education curricula. We evaluate over 20 LLMs, including multilingual open-source (e.g., Gemma, Llama, MT5), closed-source (GPT 4o, Claude, Gemini), and Turkish-adapted (e.g., Trendyol) models.
  arXiv  Detail & Related papers  (2024-07-17T08:28:55Z)
• Bridging the Bosphorus: Advancing Turkish Large Language Models through Strategies for Low-Resource Language Adaptation and Benchmarking [1.3716808114696444]
  Large Language Models (LLMs) are becoming crucial across various fields, emphasizing the urgency for high-quality models in underrepresented languages. This study explores the unique challenges faced by low-resource languages, such as data scarcity, model selection, evaluation, and computational limitations.
  arXiv  Detail & Related papers  (2024-05-07T21:58:45Z)
• The Power of Question Translation Training in Multilingual Reasoning: Broadened Scope and Deepened Insights [108.40766216456413]
  We propose a question alignment framework to bridge the gap between large language models' English and non-English performance. Experiment results show it can boost multilingual performance across diverse reasoning scenarios, model families, and sizes. We analyze representation space, generated response and data scales, and reveal how question translation training strengthens language alignment within LLMs.
  arXiv  Detail & Related papers  (2024-05-02T14:49:50Z)
• CIF-Bench: A Chinese Instruction-Following Benchmark for Evaluating the Generalizability of Large Language Models [53.9835961434552]
  We introduce the Chinese Instruction-Following Benchmark (CIF-Bench) to evaluate the generalizability of large language models (LLMs) to the Chinese language. CIF-Bench comprises 150 tasks and 15,000 input-output pairs, developed by native speakers to test complex reasoning and Chinese cultural nuances. To mitigate data contamination, we release only half of the dataset publicly, with the remainder kept private, and introduce diversified instructions to minimize score variance.
  arXiv  Detail & Related papers  (2024-02-20T16:02:12Z)
• ChEF: A Comprehensive Evaluation Framework for Standardized Assessment of Multimodal Large Language Models [49.48109472893714]
  Multimodal Large Language Models (MLLMs) have shown impressive abilities in interacting with visual content with myriad potential downstream tasks. We present the first Comprehensive Evaluation Framework (ChEF) that can holistically profile each MLLM and fairly compare different MLLMs. We will publicly release all the detailed implementations for further analysis, as well as an easy-to-use modular toolkit for the integration of new recipes and models.
  arXiv  Detail & Related papers  (2023-11-05T16:01:40Z)
• Benchmarking Procedural Language Understanding for Low-Resource Languages: A Case Study on Turkish [2.396465363376008]
  We conduct a case study on Turkish procedural texts. We first expand the number of tutorials in Turkish wikiHow from 2,000 to 52,000 using automated translation tools. We generate several downstream tasks on the corpus, such as linking actions, goal inference, and summarization.
  arXiv  Detail & Related papers  (2023-09-13T03:42:28Z)
• CMMLU: Measuring massive multitask language understanding in Chinese [133.70911295934746]
  This paper introduces a comprehensive Chinese benchmark that covers various subjects, including natural science, social sciences, engineering, and humanities. CMMLU fills the gap in evaluating the knowledge and reasoning capabilities of large language models within the Chinese context.
  arXiv  Detail & Related papers  (2023-06-15T15:49:51Z)

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.