AceGPT, Localizing Large Language Models in Arabic (2309.12053v5)

Abstract: This paper is devoted to the development of a localized LLM specifically for Arabic, a language imbued with unique cultural characteristics inadequately addressed by current mainstream models. Significant concerns emerge when addressing cultural sensitivity and local values. To address this, the paper proposes a comprehensive solution that includes further pre-training with Arabic texts, Supervised Fine-Tuning (SFT) utilizing native Arabic instructions, and GPT-4 responses in Arabic, alongside Reinforcement Learning with AI Feedback (RLAIF) employing a reward model attuned to local culture and values. The goal is to cultivate culturally cognizant and value-aligned Arabic LLMs capable of accommodating the diverse, application-specific needs of Arabic-speaking communities. Comprehensive evaluations reveal that the resulting model, dubbed `AceGPT', sets the state-of-the-art standard for open Arabic LLMs across various benchmarks. Codes, data, and models are in https://github.com/FreedomIntelligence/AceGPT.

Localization of LLMs for Arabic: AceGPT

The paper presented in the paper focuses on the development of AceGPT, a localized LLM tailored to the Arabic language, addressing the cultural and contextual nuances that are often inadequately captured by mainstream LLMs such as GPT-3.5 Turbo and GPT-4. The paper highlights the necessity of culturally adapting LLMs to meet the diverse needs of Arabic-speaking communities.

Methodological Framework

The methodology of AceGPT involves a comprehensive approach to the localization of LLMs, structured around three key strategies:

1. Localized Pre-Training: The model, based on LLaMA2, undergoes additional pre-training using a substantial corpus of Arabic text. This step is crucial for embedding the model with robust language constructs and contextual understanding specific to Arabic.
2. Localized Supervised Fine-Tuning (SFT): Fine-tuning is implemented using Arabic natural questions derived from Quora and responses generated in Arabic by GPT-4. This ensures the model's capacity to follow culturally pertinent instructions naturally and accurately.
3. Reinforcement Learning with AI Feedback (RLAIF): This involves optimizing the model's responses further using a reward model trained on localized preference data. This stage is vital in aligning the model's outputs with local cultural values and norms.

Results and Evaluation

AceGPT's performance was assessed across several benchmarks:

• Instruction-Following: Evaluated using Arabic versions of Vicuna-80 and AlpacaEval, AceGPT-13B-chat achieved a performance ratio of 100.88% relative to GPT-3.5 Turbo on Arabic Vicuna-80.
• Natural Language Understanding (NLU): AceGPT demonstrated strong capabilities in NLU tasks as seen in its second-best performance in the ALUE benchmark.
• Knowledge Benchmark: The model achieved state-of-the-art results in Arabic specific knowledge benchmarks like MMLU and EXAMs.

The improvements observed in these evaluations underscore the effectiveness of the localized training framework implemented in AceGPT, particularly in comparison with other open-source models like Jais and Phoenix.

Implications and Future Directions

The development of AceGPT emphasizes the importance of cultural and contextual adaptation in the deployment of LLMs in non-English speaking regions. By embedding culturally relevant data and preferences into the learning process, AceGPT sets a new standard for Arabic LLMs, enhancing their applicability in practical, culturally sensitive scenarios.

The implications of this work extend beyond the specific context of the Arabic language. They underscore a necessary shift towards creating more localized and context-aware AI applications. Future work could focus on expanding similar methodologies to other languages and cultural contexts, ensuring that LLMs can serve as truly inclusive tools that respect and understand the diversity of global linguistic landscapes.

In conclusion, AceGPT represents a significant step towards addressing the 'localization issue' in LLMs, providing a robust framework for aligning machine learning models with the cultural and linguistic nuances essential for practical application in diverse linguistic communities.