HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models (2405.14831v3)

Abstract: In order to thrive in hostile and ever-changing natural environments, mammalian brains evolved to store large amounts of knowledge about the world and continually integrate new information while avoiding catastrophic forgetting. Despite the impressive accomplishments, LLMs, even with retrieval-augmented generation (RAG), still struggle to efficiently and effectively integrate a large amount of new experiences after pre-training. In this work, we introduce HippoRAG, a novel retrieval framework inspired by the hippocampal indexing theory of human long-term memory to enable deeper and more efficient knowledge integration over new experiences. HippoRAG synergistically orchestrates LLMs, knowledge graphs, and the Personalized PageRank algorithm to mimic the different roles of neocortex and hippocampus in human memory. We compare HippoRAG with existing RAG methods on multi-hop question answering and show that our method outperforms the state-of-the-art methods remarkably, by up to 20%. Single-step retrieval with HippoRAG achieves comparable or better performance than iterative retrieval like IRCoT while being 10-30 times cheaper and 6-13 times faster, and integrating HippoRAG into IRCoT brings further substantial gains. Finally, we show that our method can tackle new types of scenarios that are out of reach of existing methods. Code and data are available at https://github.com/OSU-NLP-Group/HippoRAG.

• The paper introduces HippoRAG, a neurobiologically inspired framework that efficiently integrates evolving knowledge in LLMs.
• The paper combines knowledge graphs with the Personalized PageRank algorithm to achieve up to 20% higher retrieval accuracy and 6-13 times faster responses.
• The paper demonstrates that offline indexing with single-step online retrieval mitigates catastrophic forgetting, enhancing multi-hop question answering.

HippoRAG: Efficient Knowledge Integration in LLMs

Integrating evolving knowledge while mitigating catastrophic forgetting remains a critical challenge for LLMs. This paper introduces HippoRAG, an advanced retrieval framework inspired by the hippocampal indexing theory, aiming to enhance knowledge integration capabilities for LLMs. Combining LLMs, knowledge graphs (KGs), and the Personalized PageRank (PPR) algorithm, HippoRAG emulates human memory mechanisms and sets new benchmarks in multi-hop question answering (QA).

Introduction

Current LLMs, despite their notable advancements, exhibit significant limitations in maintaining and updating a long-term memory. Retrieval-augmented generation (RAG) has emerged as a primary method to address this deficiency, enabling the incorporation of new information post-training. However, existing RAG systems are constrained by their inability to integrate knowledge effectively across multiple passages without significant computational overhead. This paper proposes HippoRAG, a novel framework modeled after the hippocampal memory indexing theory, to address these integration challenges more efficiently and accurately.

Methodology

HippoRAG's approach involves two distinct processes: offline indexing and online retrieval. During offline indexing, an LLM processes the retrieval corpus passages to extract noun phrases and construct a schemaless KG, functioning as an artificial hippocampal index. Additional edges are added based on synonymy detected by pre-trained retrieval encoders, enhancing the KGs connectivity.

In the online retrieval phase, key named entities from a query are identified using the same LLM. These entities serve as seeds for the PPR algorithm on the KG, which retrieves contextually relevant subgraphs in a single step. This methodology leverages graph associations akin to neural connections in the hippocampus, thus facilitating efficient multi-hop reasoning.

Results

Performance Metrics: HippoRAG demonstrates significant performance gains over existing RAG methods across various benchmarks. Notably, on the MuSiQue and 2WikiMultiHopQA datasets, it outperforms state-of-the-art methods by margins up to 20% in retrieval accuracy. Additionally, HippoRAG achieves comparable results on the simpler HotpotQA dataset, validating its robustness.

Efficiency: The single-step retrieval mechanism of HippoRAG is 10-30 times cheaper and 6-13 times faster than iterative retrieval methods like IRCoT. Combining HippoRAG with IRCoT further enhances retrieval accuracy, showcasing the complementary strengths of the two methods.

Implications

Theoretical Implications: HippoRAG offers a biologically grounded strategy for mitigating catastrophic forgetting in LLMs by drawing parallels with human memory systems. This hybrid approach emphasizes the utility of structured representations (KGs) combined with modern retrieval algorithms (PPR), potentially inspiring future work that integrates cognitive science principles into AI development.

Practical Implications: HippoRAG's improvements in both accuracy and efficiency make it a highly practical solution for applications requiring dynamic and reliable knowledge integration. Domains such as legal research, medical diagnostics, and scientific literature review can benefit significantly from this methodology, facilitating better decision-making and knowledge access.

Future Directions

While HippoRAG presents considerable advancements, several areas offer potential for further exploration:

1. Component Fine-Tuning: Improved performance could be achieved through targeted fine-tuning of the LLMs used for NER and OpenIE tasks.
2. Enhanced Graph Search: Future work could explore more sophisticated graph traversal methods beyond PPR to better handle complex knowledge integration tasks.
3. Scalability: The framework’s scalability to much larger datasets needs evaluation, particularly considering the computational costs and storage requirements of extensive KGs.

Conclusion

HippoRAG emerges as a formidable contender in the quest for efficient knowledge integration in LLMs, with strong theoretical underpinnings and practical advantages. By mimicking human memory mechanisms and utilizing sophisticated retrieval techniques, it presents a balanced solution that bridges the gap between static knowledge retention and dynamic information assimilation. Continued exploration and refinement of this framework could pave the way for even more robust and adaptive AI systems.