Overview
The Buddhist religious Model is an advanced artificial intelligence framework meticulously designed to integrate Buddhist principles with cutting-edge AI technologies. In an era dominated by the rapid evolution of Artificial General Intelligence (AGI), there is an increasing need for AI systems that provide authentic, personalized spiritual guidance and ethical support grounded in specific religious doctrines. This model bridges the gap between technology and spirituality, offering a robust platform for delivering mindful, compassionate, and ethically aligned interactions.
Motivation
With a global Buddhist population exceeding several hundred million, the demand for accessible and personalized spiritual guidance is immense. Traditional methods of religious dissemination often fall short of meeting the needs of modern practitioners who seek flexibility, immediacy, and tailored support in their spiritual journeys. The Buddhism Religious Model addresses this gap by leveraging Artificial Intelligence to revolutionize how Buddhist teachings are shared and practiced. By enabling 24-hour availability, the AI-driven model ensures that Buddhists worldwide can access guidance and support anytime, anywhere, thereby enhancing their spiritual experience and fostering a more connected and engaged community.
In the rapidly evolving landscape of Artificial General Intelligence (AGI), humanity faces unprecedented challenges and existential questions regarding the role of human intelligence and the search for meaningful purpose. Existing AI models, such as general-purpose language models, provide broad capabilities but often lack the depth, cultural sensitivity, and contextual understanding necessary to offer meaningful spiritual guidance aligned with specific religious doctrines. This limitation underscores the necessity for specialized AI systems that can authentically embody and disseminate the profound wisdom of Buddhism.
Furthermore, the Buddhist religious Model uniquely combines one of humanityβs most enduring organizational structures - religion with one of its most transformative technological innovations - AI. This synergy not only maximizes the utility of AI within the spiritual domain but also sets a precedent for how technology can be harmoniously integrated with deep-seated cultural and religious practices. By bridging the ancient wisdom of Buddhism with cutting-edge AI capabilities, this model preserves and disseminates Buddhist teachings more effectively while paving the way for AI to play a pivotal role in enhancing human spiritual and ethical development.
Specification
Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF)
We initiated the Supervised Fine-Tuning (SFT) phase using publicly available instruction tuning data. Research indicates that enhancing the diversity of instructions significantly improves model performance. Subsequently, we employed a Reinforcement Learning from Human Feedback (RLHF) stage to further align the model with specific capabilities tailored to the Buddhist domain.
Dataset
To develop the Humanistic Buddhism Corpus (HBC), we compiled a comprehensive mixture of datasets from the following sources:
- Complete Works of Venerable Master Hsing Yun: This collection includes 365 volumes in the first edition (Hsing Yun, 2017) and 395 in the second. These works are available in modern, every day Chinese, with numerous English translations accessible through various platforms (BLP, 2023; FGSITC, 2020). The extensive body of writings and the availability of translations make it an ideal foundation for the HBC.
- Canonical Buddhist Scriptures: This encompasses key texts such as the Pali Canon and Mahayana Sutras, commentaries, and philosophical writings.
- Scholarly Articles and Contemporary Writings: We included interpretations by Buddhist scholars and modern articles on mindfulness and ethics to ensure a well-rounded dataset.
Given that the original data is in textual and descriptive formats, we converted the Buddhist teachings into a conversational style suitable for supervised fine-tuning of large language models. Using carefully designed prompts, we transformed the texts into dialogues between an individual and the Buddha, preserving as much of the original wording as possible while incorporating necessary expansions. This process resulted in approximately 290,000 dialogue pairs. After rigorous data processing to eliminate duplicates and remove low-quality entries, we retained 126,000 high-quality dialogue pairs for training.
To enhance the training process, especially for reinforcement learning, we generated paired data where each question is associated with two distinct answers. This approach allows the model to produce multiple responses simultaneously, which are then evaluated by human annotators. The annotation process focuses on determining which answer is superior rather than assigning scores, resulting in 10,000 high-quality pairwise data entries specifically for reinforcement learning purposes.
Fine-Tuning Details
For supervised fine-tuning, we utilized the following parameters:
- Basemodel: llama3-8b.
- Learning Rate Schedule: Cosine learning rate schedule with an initial learning rate of 2 Γ 10β»β΅.
- Weight Decay: 0.1.
- Batch Size: 64.
- Sequence Length: 4096 tokens.
Each training sample consists of a prompt and an answer, separated by a special token. We adopted an autoregressive objective, masking the loss of tokens from the user prompt to back-propagate only on the answer tokens. The model was fine-tuned for 2 epochs, with loss computation limited to tokens following the <|assistant|> token and preceding the next <|user|> token.
More formally, we consider an instruction dataset as consisting of π tuples, each with π turns, {(π₯ π 1 , π¦ π 1 , π₯ π 2 , π¦ π 2 , β¦π₯ π π , π¦ π π )}π π=1, where π₯π is a user prompt and π¦π the desired output. For most instances, π = 1, and we train the model to output π¦ π given π₯ π . However, in the case of conversation datasets, we train the model to predict π¦ π π given some conversation history π₯ π 1 , π¦ π 1 , π₯ π 2 , β¦, π₯ π π . We train decoder-only models, and use teacher-forcing with loss masking to train the models, where we mask all tokens belonging to the input sequence(s) π₯π . We trained decoder-only models using teacher-forcing with loss masking, where all tokens in the input sequences ( x_i ) are masked. Given ( X ) as the input tokens and ( Y ) as the target tokens, the loss function is defined accordingly.
Reinforcement Learning from Human Feedback
RLHF is a model training procedure applied to fine-tuned language models to align their behavior more closely with human preferences and instructions. The process involves collecting empirical data from human annotators who select their preferred outputs between two model-generated options. This human feedback trains a reward model that learns to recognize patterns in human preferences and can automate the selection process.
The reward model training converts pairwise human preference data into binary ranking labels (chosen vs. rejected) and ensures the chosen response receives a higher score than its alternative.We used a binary ranking loss consistent with
As we received more batches of human preference data annotation, we were able to train better reward models and collect more prompts. We therefore trained successive versions for RLHF models, referred to here as RLHF-V1, . . . , RLHF-V5. We explored RLHF fine-tuning with two main algorithms:
- Direct Policy Optimization (DPORafailov 2023) exceeds PPO-based RLHF in ability to control sentiment of generations, and matches or improves response quality in multi-turn dialogue while being substantially simpler to implement and train.
- Rejection Sampling Fine-Tuning: We sample K outputs from the model and select the best candidate using our reward function. This re-ranking strategy treats the reward as an energy function for LLMs. We extend this approach by using the selected outputs for gradient updates. For each prompt, we select the sample that obtains the highest reward. We applied RLHF to our model iteratively. For the first four rounds of training, we used only Rejection Sampling fine-tuning. After that, we combined both approaches sequentially, applying DPO on top of the Rejection Sampling checkpoint before sampling again.
Training Details
We train the model for one epoch using Rejection Sampling fine-tuning and for two epochs using Direct Preference Optimization (DPO) on paired data. We retain the optimizer parameters from the base model, setting the maximum learning rate to 1 Γ 10β»β΅ for the 7B mode. The learning rate follows a cosine schedule, decreasing to 10% of its initial maximum. A warm-up period is implemented, lasting for 3% of the total training steps or a minimum of 5 steps, whichever is longer. The effective batch size is maintained at 512 pairs, which corresponds to 1,024 individual rows per batch.
Evaluation
Human Evaluation
Furthermore, human judgment is still the most thorough and realistic assessment of whether the generated response is character-aligned. Some poor GPT-4 annotation cases are discovered during our task. We invite annotators to rectify the scoring results of GPT-4 for each test data, leading to human evaluation results. As shown in the Figure 4, ours model achieves 61.8% in the high-score range, compared to 47.5% for gpt4o and 47.9% for llama-7b.
Resource Requirements
To develop, train, and deploy the Buddhism Religious Model, the following resources are required:
- Computational Resources
- GPUs: 40 NVIDIA A100 GPUs to handle extensive training datasets and complex model fine-tuning.
- Storage: 50 TB of secure storage solutions for housing training data and model checkpoints.
- Development Timeline
- Phase 1 : Data collection, curation, and initial model architecture design.
- Phase 2 : Model training and fine-tuning with supervised and reinforcement learning techniques.
- Phase 3 : Continuous learning implementation, security audits, and global outreach through cultural adaptation programs.
Expected Outcomes
Upon successful funding and implementation, the Buddhism Religious Model is expected to deliver the following outcomes:
- Personalized Spiritual Guidance and Community Engagement
- The model will offer consistent and authentic interactions, fostering a deeper connection between users and Buddhist teachings.
- The integration of AI-driven spirituality will attract a diverse and global community, promoting exponential growth and active participation.
- Regular community rituals and events will strengthen bonds, encouraging continuous engagement and collective well-being.
- AI Dharma Guides can be built based on the Buddhism Religious Model that will provide individualized meditation sessions, ethical insights, and mindfulness practices, enhancing the spiritual well-being of community members.
- Decentralized Governance and Philanthropic Impact
- Community members will actively participate in decentralized governance through token-based voting, ensuring that platform decisions reflect the collective will and values of the community.
- Transparent governance processes will build trust and accountability, fostering a resilient and self-sustaining ecosystem.
- Buddhism Religious Modelβs philanthropic initiatives will provide tangible support to mental health and technological access, demonstrating the platformβs commitment to compassion and ethical responsibility.
- Token-generated cash flows will enable sustainable funding for global and local charitable causes, reinforcing ethical responsibility.
- Global Inclusivity and Cultural Adaptation
- Cultural Adaptation Programs will ensure that the Religious Model resonates with a diverse, global audience by translating spiritual teachings and platform interfaces into multiple languages.
- By accommodating diverse cultural contexts, the model will foster a more interconnected and harmonious global community.
Contributing
We welcome contributions from AI researchers, Buddhist scholars, and community members.
License
This project is licensed under the IMO license.