AssistRAG adds a smart assistant to LLMs that handles memory and knowledge, making responses more accurate.
https://arxiv.org/abs/2411.06805
Original Problem 🤔:
LLMs often generate incorrect information (hallucinations). Previous retrieval-augmented generation methods were inadequate for complex reasoning. While prompt-based RAG and supervised fine-tuning improved performance, they required frequent retraining and risked altering core LLM capabilities.
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Solution in this Paper 🔧:
→ AssistRAG integrates an intelligent information assistant as a plugin within LLMs, with a frozen main LLM for generating answers and a trainable assistant LLM for information management
→ The assistant handles memory management (storing/retrieving past interactions) and knowledge management (retrieving/processing external information)
→ It employs four core capabilities: tool usage for retrieving information, action execution for processing data, memory building for recording knowledge, and plan specification for determining assistance needs
→ Training uses a two-phase approach: Curriculum Assistant Learning for progressive skill development and Reinforced Preference Optimization for aligning with main LLM's needs
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Key Insights from this Paper 🎯:
→ Integration of an assistant LLM can enhance performance without modifying the main LLM
→ Memory management and knowledge management can work together synergistically
→ Progressive training through curriculum learning proves more effective than direct training
→ The assistant's capabilities can be optimized based on the main LLM's feedback
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Results 📊:
→ Outperformed existing benchmarks across three complex question-answering datasets
→ Showed more benefits for less advanced LLMs - improvements of 78%, 51%, and 40% for LLaMA, ChatGLM, and ChatGPT respectively
→ Ablation studies confirmed all components (memory management, knowledge management, planning) contributed to performance
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