Towards Truly Open, Language-Specific, Safe, Factual, and Specialized Large Language Models
Nakov, Preslav
Nakov, Preslav
Author
Supervisor
Department
Natural Language Processing
Embargo End Date
Type
Workshop
Date
2025
License
Language
English
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Research Projects
Organizational Units
Journal Issue
Abstract
First, we will argue for the need for fully transparent open-source large language models (LLMs), and we will describe the efforts of MBZUAI’s Institute on Foundation Models (IFM) towards that based on the LLM360 initiative. Second, we will argue for the need for language-specific LLMs, and we will share our experience from building Jais, the world’s leading open Arabic-centric foundation and instruction-tuned large language model, Nanda, our recently released open Hindi LLM, and some other models. Third, we will argue for the need for safe LLMs, and we will present Do-Not-Answer, a dataset for evaluating the guardrails of LLMs, which is at the core of the safety mechanisms of our LLMs. Forth, we will argue for the need for factual LLMs, we will discuss the factuality challenges that LLMs pose. We will then present some recent relevant tools for addressing these challenges developed at MBZUAI: (i) OpenFactCheck, a framework for fact-checking LLM output, for building customized fact-checking systems, and for benchmarking LLMs for factuality, (ii) LM-Polygraph, a tool for predicting an LLM’s uncertainty in its output using cheap and fast uncertainty quantification techniques, and (iii) LLM-DetectAIve, a tool for machine-generated text detection. Finally, we will argue for the need for specialized models, and we will present the zoo of LLMs currently being developed at MBZUAI’s IFM.
Citation
P. Nakov, “Towards Truly Open, Language-Specific, Safe, Factual, and Specialized Large Language Models,” 2025. Accessed: Mar. 12, 2025. [Online]. Available: https://aclanthology.org/2025.bucc-1.3/
Source
Proceedings of the 18th Workshop on Building and Using Comparable Corpora (BUCC)
Conference
Keywords
Open-source large language models, Language-specific LLMs, LLM safety evaluation, Fact-checking frameworks, Machine-generated text detection
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Source
Publisher
Association for Computational Linguistics