RAFT : Reinforcement Learning with Rag based Finetunning
Why We Need RAFT: Adapting Language Models to Domain-Specific RAG​
The evolution of Retrieval-Augmented Generation (RAG) has unlocked unprecedented possibilities in AI, enabling generative models to retrieve and incorporate external data dynamically. However, as AI frameworks increasingly interface with domain-specific contexts like Web3, there is a growing need for a specialized adaptation mechanism—RAFT (Retrieval-Adapted Fine-Tuning). This blog explores why RAFT is essential for adapting language models to domain-specific RAG, enhancing real-time interactions with the Web3 community and its users.
The Challenge: Domain-Specificity in RAG​
Web3 ecosystems are inherently dynamic and domain-specific, characterized by:
- Unique Jargon and Concepts: Terms like "staking," "DAO," "NFT minting," and "gas fees" are ubiquitous in Web3 but rarely encountered in general-purpose datasets.
- Rapidly Evolving Information: Web3 platforms are continuously updated with new protocols, smart contracts, and token standards.
- Decentralized Data Sources: Information is dispersed across blockchains, decentralized file systems, and community-managed repositories.
While RAG frameworks excel in retrieving relevant data, they often struggle with adapting generative outputs to these domain-specific requirements. Without fine-tuning, language models risk producing generic or irrelevant responses that fail to meet the expectations of Web3 users.
Enter RAFT: Retrieval-Adapted Fine-Tuning​
RAFT bridges the gap between general-purpose language models and domain-specific RAG systems. It fine-tunes generative AI models based on:
- Domain-Specific Retrieval Feedback: Using real-time feedback loops from RAG outputs to iteratively improve model understanding of domain-specific data.
- Contextual Adaptation: Embedding domain-specific knowledge directly into the model, ensuring outputs align with Web3 terminology and concepts.
- Real-Time Interaction: Enabling models to dynamically adapt to the evolving nature of decentralized communities.
Why RAFT is Essential for Web3​
1. Enhanced Domain Understanding​
By fine-tuning on retrievals from Web3-specific datasets, RAFT ensures:
- Accurate interpretations of blockchain data.
- Consistent use of Web3 terminology.
- Contextually relevant responses to user queries.
2. Improved User Experience​
RAFT reduces response errors and latency, providing Web3 users with:
- Precise answers to technical questions (e.g., "How do I connect my wallet?").
- Contextualized insights from decentralized governance proposals.
3. Adaptation to Rapid Change​
With RAFT, models can:
- Quickly incorporate updates from newly launched protocols.
- Adjust to shifts in community discourse and trending topics.
4. Scalability for Decentralized Data​
Using RAFT, language models can seamlessly interface with decentralized storage solutions like IPFS and Arweave, as well as multi-modal vector databases like Milvus.
How RAFT Works​
- Data Collection:
- Gather domain-specific datasets from decentralized sources, including blockchain data, community forums, and project whitepapers.
- Domain-Specific Retrieval:
- Leverage RAG frameworks to fetch relevant data dynamically.
- Fine-Tuning:
- Adapt language models using retrieval outputs, incorporating:
- Lexical and semantic feedback.
- Domain-specific annotations.
- Task-specific prompts (e.g., transaction analysis, community Q&A).
- Adapt language models using retrieval outputs, incorporating:
- Evaluation and Feedback:
- Iteratively evaluate model outputs using metrics like BLEU, ROUGE, and human validation.
Real-World Applications​
1. Decentralized Governance Support:​
- RAFT enables accurate summarization of proposals from DAOs, fostering informed decision-making within communities.
2. Web3 Customer Support:​
- RAFT-enhanced chatbots can address user queries about wallets, transactions, and staking mechanisms with precision.
3. Multi-Chain Interoperability:​
- Facilitates understanding of cross-chain protocols, improving developer support for interoperability solutions.
References​
- "Fine-Tuning Large Vision-Language Models as Decision-Making Agents via Reinforcement Learning." Retrieved from arXiv Link
- "Relation Extraction with Fine-Tuned Large Language Models in Retrieval Augmented Frameworks." Retrieved from arXiv Link
- "Improving Large Language Models via Fine-grained Reinforcement Learning with Minimum Editing Constraint." Retrieved from arXiv Link
- "OG-RAG: Ontology-Grounded Retrieval-Augmented Generation For Large Language Models." Retrieved from arXiv Link
- "Teaching Large Language Models to Reason with Reinforcement Learning." Retrieved from arXiv Link